library(MASS) 
library(carData)
library(car) 
library(fitdistrplus)
library(goft)

1 Read data

First Mediterranean data:

data_M<- read.csv("MED_2017-21.csv",sep=";",dec=",")
head(data_M)
##          Covid       date       specie     euros      kgs
## 1 Reference No 02/01/2017 ANE-BOQUERON    830.60   111.69
## 2 Reference No 02/01/2017  PIL-SARDINA 129131.37 54232.49
## 3 Reference No 03/01/2017 ANE-BOQUERON    749.13    77.00
## 4 Reference No 03/01/2017  PIL-SARDINA  81008.61 45355.49
## 5 Reference No 04/01/2017  PIL-SARDINA  42228.58 21619.01
## 6 Reference No 05/01/2017 ANE-BOQUERON     28.98     6.85
library(dplyr)
## 
## Attaching package: 'dplyr'
## The following object is masked from 'package:car':
## 
##     recode
## The following object is masked from 'package:MASS':
## 
##     select
## The following object is masked from 'package:nlme':
## 
##     collapse
## The following objects are masked from 'package:stats':
## 
##     filter, lag
## The following objects are masked from 'package:base':
## 
##     intersect, setdiff, setequal, union
data_M$euros=data_M$euros/data_M$kgs
e=data_M %>%
     group_by(specie) %>%
     summarise(Mean = mean(euros, na.rm = TRUE))
e$Mean=round(e$Mean,1);e
## # A tibble: 28 x 2
##    specie                       Mean
##    <chr>                       <dbl>
##  1 ANE-BOQUERON                  3.2
##  2 ARA-GAMBA ROJA O RAYADO      37.6
##  3 BFT-ATUN ROJO                 9.4
##  4 BLT-MELVA                     2  
##  5 BOG-BOGA                      0.4
##  6 BON-BONITO O BONITO DEL SUR   5  
##  7 CET-ACEDIA                    7.9
##  8 DPS-GAMBA                    12.9
##  9 FOR-BROTOLA DE ROCA           7.4
## 10 FRZ-MELVAS                    2.3
## # ... with 18 more rows
e=e[c(21,1,6,7,8,9,10,4,16,20,23,26,12,5,2,24,3),]
l=data_M %>%
     group_by(specie) %>%
     summarise(Mean = mean(kgs, na.rm = TRUE))
l$Mean=round(l$Mean,1);l
## # A tibble: 28 x 2
##    specie                        Mean
##    <chr>                        <dbl>
##  1 ANE-BOQUERON                7882. 
##  2 ARA-GAMBA ROJA O RAYADO      528. 
##  3 BFT-ATUN ROJO               1869. 
##  4 BLT-MELVA                   2897. 
##  5 BOG-BOGA                    1455. 
##  6 BON-BONITO O BONITO DEL SUR  393. 
##  7 CET-ACEDIA                     5  
##  8 DPS-GAMBA                   1449. 
##  9 FOR-BROTOLA DE ROCA           31.2
## 10 FRZ-MELVAS                  4602. 
## # ... with 18 more rows
l=l[c(21,1,6,7,8,9,10,4,16,20,23,26,12,5,2,24,3),]

Second Golf of Cádiz data:

data_A<- read.csv("ATL_2017-21.csv",sep=";",dec=",")
head(data_A)
##          Covid       date       specie    euros      kgs
## 1 Reference No 09/01/2017  PIL-SARDINA 37655.47 15055.34
## 2 Reference No 10/01/2017  PIL-SARDINA 41208.45 29884.03
## 3 Reference No 11/01/2017 ANE-BOQUERON 22767.86  4288.60
## 4 Reference No 11/01/2017  PIL-SARDINA 13636.23  7347.15
## 5 Reference No 12/01/2017 ANE-BOQUERON 51289.61 10756.45
## 6 Reference No 12/01/2017  PIL-SARDINA  2415.19  1624.13
data_A$euros=data_A$euros/data_A$kgs
e=data_A%>%
     group_by(specie) %>%
     summarise(Mean = mean(euros, na.rm = TRUE))
e$Mean=round(e$Mean,1);e
## # A tibble: 27 x 2
##    specie                       Mean
##    <chr>                       <dbl>
##  1 ANE-BOQUERON                  2.6
##  2 ARA-GAMBA ROJA O RAYADO      36.5
##  3 BFT-ATUN ROJO                13.2
##  4 BLT-MELVA                     1.8
##  5 BOG-BOGA                      0.5
##  6 BON-BONITO O BONITO DEL SUR   3.3
##  7 CET-ACEDIA                    9.1
##  8 DPS-GAMBA                     9.1
##  9 FOR-BROTOLA DE ROCA           5.6
## 10 FRZ-MELVAS                    1.8
## # ... with 17 more rows
e=e[c(20,1,6,7,8,9,10,4,15,19,22,25,12,5,2,23,3),]
l=data_A %>%
     group_by(specie) %>%
     summarise(Mean = mean(kgs, na.rm = TRUE))
l$Mean=round(l$Mean);l
## # A tibble: 27 x 2
##    specie                       Mean
##    <chr>                       <dbl>
##  1 ANE-BOQUERON                29424
##  2 ARA-GAMBA ROJA O RAYADO       330
##  3 BFT-ATUN ROJO               13644
##  4 BLT-MELVA                      87
##  5 BOG-BOGA                     1228
##  6 BON-BONITO O BONITO DEL SUR   656
##  7 CET-ACEDIA                    649
##  8 DPS-GAMBA                   12854
##  9 FOR-BROTOLA DE ROCA            84
## 10 FRZ-MELVAS                    110
## # ... with 17 more rows
l=l[c(20,1,6,7,8,9,10,4,15,19,22,25,12,5,2,23,3),]

2 Golf of Cádiz

2.1 Octopus vulgaris

data1=subset(data_A,data_A$specie=="OCC-PULPO DE ROCA O PULPO ROQUERO")

Is gamma a correct distribution for the response variable PRICE?

summary(data1$euros)
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##   2.500   5.013   5.327   5.605   5.778  11.535
a=fitdist(data1$euros, distr = "gamma",start=list(shape = 1, rate = 2), lower = -1,method = "mme")
plot(a)

ks.test(na.omit(data1$euros), "pgamma", a$estimate[1],a$estimate[2])
## 
##  One-sample Kolmogorov-Smirnov test
## 
## data:  na.omit(data1$euros)
## D = 0.16061, p-value = 8.882e-16
## alternative hypothesis: two-sided
gamma_test(data1$euros)
## 
##  Test of fit for the Gamma distribution
## 
## data:  data1$euros
## V = 18.986, p-value < 2.2e-16

Is gamma a correct distribution for the response variable ABUNDANCE?

summary(data1$kgs)
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##     6.3   542.1  5481.5  7056.6  9601.0 62604.0
a=fitdist(data1$kgs, distr = "gamma",start=list(shape = 1, rate = 2), lower = -1,method = "mme")
plot(a)

ks.test(na.omit(data1$kgs), "pgamma", a$estimate[1],a$estimate[2])
## 
##  One-sample Kolmogorov-Smirnov test
## 
## data:  na.omit(data1$kgs)
## D = 0.10729, p-value = 2.83e-07
## alternative hypothesis: two-sided
gamma_test(data1$kgs)
## 
##  Test of fit for the Gamma distribution
## 
## data:  data1$kgs
## V = -2.2071, p-value = 0.1186

Regression models

x=as.factor(data1$Covid)
levels(x)
## [1] "after state of alarm"           "Reference after state of alarm"
## [3] "Reference No"                   "Reference state of alarm 1"    
## [5] "Reference state of alarm 2"     "state of alarm 1"              
## [7] "state of alarm 2"
data0=subset(data1,data1$Covid=="Reference state of alarm 1" | data1$Covid=="state of alarm 1")
boxplot(data0$euros~data0$Covid, col = rgb(0, 0.5, 1, alpha = 0.5),xlab="Covid Category",ylab="Price")

boxplot(data0$kgs~data0$Covid, col = rgb(0, 0.5, 1, alpha = 0.5),xlab="Covid Category",ylab="Abundance")

x=as.factor(data0$Covid)
table(x)
## x
## Reference state of alarm 1           state of alarm 1 
##                         75                         79
y=as.numeric(data0$euros)
x=relevel(x, ref = "Reference state of alarm 1")
z=as.numeric(data0$kgs)

Model0<-glm((y)~x,family=Gamma(link=log))
cooksd <- cooks.distance(Model0)
influential <- cooksd[(cooksd > (3 * mean(cooksd, na.rm = TRUE)))]
influential=as.numeric(names(influential))
y=y[-influential]
xaux=x[-influential]
Model0<-glm((y)~xaux,family=Gamma(link=log))
summary(Model0)
## 
## Call:
## glm(formula = (y) ~ xaux, family = Gamma(link = log))
## 
## Deviance Residuals: 
##      Min        1Q    Median        3Q       Max  
## -0.19902  -0.06195  -0.01543   0.05736   0.26905  
## 
## Coefficients:
##                      Estimate Std. Error t value Pr(>|t|)    
## (Intercept)           1.67550    0.01067 157.102  < 2e-16 ***
## xauxstate of alarm 1 -0.06707    0.01514  -4.431 1.87e-05 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for Gamma family taken to be 0.008189537)
## 
##     Null deviance: 1.2774  on 142  degrees of freedom
## Residual deviance: 1.1167  on 141  degrees of freedom
## AIC: 186.65
## 
## Number of Fisher Scoring iterations: 3
Model0<-glm((z)~x,family=Gamma(link=log))
cooksd <- cooks.distance(Model0)
influential <- cooksd[(cooksd > (3 * mean(cooksd, na.rm = TRUE)))]
influential=as.numeric(names(influential))
z=z[-influential]
xaux=x[-influential]

Model0<-glm((z)~xaux,family=Gamma(link=log))
summary(Model0)
## 
## Call:
## glm(formula = (z) ~ xaux, family = Gamma(link = log))
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -3.3390  -1.5572  -0.0277   0.5220   1.3358  
## 
## Coefficients:
##                      Estimate Std. Error t value Pr(>|t|)    
## (Intercept)            8.7988     0.1039  84.719   <2e-16 ***
## xauxstate of alarm 1  -0.3246     0.1431  -2.268   0.0248 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for Gamma family taken to be 0.7658476)
## 
##     Null deviance: 259.70  on 149  degrees of freedom
## Residual deviance: 255.75  on 148  degrees of freedom
## AIC: 2884.2
## 
## Number of Fisher Scoring iterations: 7
data01=subset(data1,data1$Covid=="Reference state of alarm 2" | data1$Covid=="state of alarm 2")
boxplot(data01$euros~data01$Covid, col = rgb(0, 0.5, 1, alpha = 0.5),xlab="Covid Category",ylab="Price")

boxplot(data01$kgs~data01$Covid, col = rgb(0, 0.5, 1, alpha = 0.5),xlab="Covid Category",ylab="Abundance")

x=as.factor(data01$Covid)
table(x)
## x
## Reference state of alarm 2           state of alarm 2 
##                        111                        161
y=as.numeric(data01$euros)
x=relevel(x, ref = "Reference state of alarm 2")
z=as.numeric(data01$kgs)



Model01<-glm((y)~x,family=Gamma(link=log))
cooksd <- cooks.distance(Model01)
influential <- cooksd[(cooksd > (3 * mean(cooksd, na.rm = TRUE)))]
influential=as.numeric(names(influential))
y=y[-influential]
xaux=x[-influential]
Model01<-glm((y)~xaux,family=Gamma(link=log))
summary(Model01)
## 
## Call:
## glm(formula = (y) ~ xaux, family = Gamma(link = log))
## 
## Deviance Residuals: 
##      Min        1Q    Median        3Q       Max  
## -0.29748  -0.09188  -0.01463   0.04200   0.38661  
## 
## Coefficients:
##                      Estimate Std. Error t value Pr(>|t|)    
## (Intercept)           1.63300    0.01341 121.759  < 2e-16 ***
## xauxstate of alarm 2  0.06628    0.01713   3.869  0.00014 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for Gamma family taken to be 0.0172679)
## 
##     Null deviance: 4.2068  on 247  degrees of freedom
## Residual deviance: 3.9496  on 246  degrees of freedom
## AIC: 509.95
## 
## Number of Fisher Scoring iterations: 4
Model01<-glm((z)~x,family=Gamma(link=log))
cooksd <- cooks.distance(Model01)
influential <- cooksd[(cooksd > (3 * mean(cooksd, na.rm = TRUE)))]
influential=as.numeric(names(influential))
z=z[-influential]
xaux=x[-influential]

Model01<-glm((z)~xaux,family=Gamma(link=log))
summary(Model01)
## 
## Call:
## glm(formula = (z) ~ xaux, family = Gamma(link = log))
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -3.4074  -1.1779   0.0938   0.4779   1.3848  
## 
## Coefficients:
##                      Estimate Std. Error t value Pr(>|t|)    
## (Intercept)           8.58540    0.06849 125.357   <2e-16 ***
## xauxstate of alarm 2  0.09665    0.09021   1.071    0.285    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for Gamma family taken to be 0.5206495)
## 
##     Null deviance: 434.60  on 261  degrees of freedom
## Residual deviance: 434.01  on 260  degrees of freedom
## AIC: 5043.6
## 
## Number of Fisher Scoring iterations: 6
data02=subset(data1,data1$Covid=="Reference after state of alarm" | data1$Covid=="after state of alarm")
boxplot(data02$euros~data02$Covid, col = rgb(0, 0.5, 1, alpha = 0.5),xlab="Covid Category",ylab="Price")

boxplot(data02$kgs~data02$Covid, col = rgb(0, 0.5, 1, alpha = 0.5),xlab="Covid Category",ylab="Abundance")

x=as.factor(data02$Covid)
table(x)
## x
##           after state of alarm Reference after state of alarm 
##                             96                            103
y=as.numeric(data02$euros)
x=relevel(x, ref = "Reference after state of alarm")
z=as.numeric(data02$kgs)

Model02<-glm((y)~x,family=Gamma(link=log))
cooksd <- cooks.distance(Model02)
influential <- cooksd[(cooksd > (3 * mean(cooksd, na.rm = TRUE)))]
influential=as.numeric(names(influential))
y=y[-influential]
xaux=x[-influential]
Model02<-glm((y)~xaux,family=Gamma(link=log))
summary(Model02)
## 
## Call:
## glm(formula = (y) ~ xaux, family = Gamma(link = log))
## 
## Deviance Residuals: 
##      Min        1Q    Median        3Q       Max  
## -0.18195  -0.06798  -0.01654   0.04915   0.25710  
## 
## Coefficients:
##                          Estimate Std. Error t value Pr(>|t|)    
## (Intercept)              1.704552   0.009939  171.50  < 2e-16 ***
## xauxafter state of alarm 0.056910   0.014371    3.96 0.000107 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for Gamma family taken to be 0.009482818)
## 
##     Null deviance: 1.8197  on 183  degrees of freedom
## Residual deviance: 1.6709  on 182  degrees of freedom
## AIC: 298.98
## 
## Number of Fisher Scoring iterations: 4
Model02<-glm((z)~x,family=Gamma(link=log))
cooksd <- cooks.distance(Model02)
influential <- cooksd[(cooksd > (3 * mean(cooksd, na.rm = TRUE)))]
influential=as.numeric(names(influential))
z=z[-influential]
xaux=x[-influential]

Model02<-glm((z)~xaux,family=Gamma(link=log))
summary(Model02)
## 
## Call:
## glm(formula = (z) ~ xaux, family = Gamma(link = log))
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -3.0236  -1.7309  -0.9757   0.7842   1.7207  
## 
## Coefficients:
##                          Estimate Std. Error t value Pr(>|t|)    
## (Intercept)                9.1006     0.1124  80.989   <2e-16 ***
## xauxafter state of alarm  -1.5372     0.1606  -9.569   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for Gamma family taken to be 1.199524)
## 
##     Null deviance: 515.85  on 185  degrees of freedom
## Residual deviance: 416.35  on 184  degrees of freedom
## AIC: 3436
## 
## Number of Fisher Scoring iterations: 7

2.2 Engraulis encrasicolus

data1=subset(data_A,data_A$specie=="ANE-BOQUERON")

Is gamma a correct distribution for the response variable PRICE?

summary(data1$euros)
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##  0.5219  1.4497  2.1756  2.6123  3.3159 11.8728
a=fitdist(data1$euros, distr = "gamma",start=list(shape = 1, rate = 2), lower = -1,method = "mme")
plot(a)

ks.test(na.omit(data1$euros), "pgamma", a$estimate[1],a$estimate[2])
## 
##  One-sample Kolmogorov-Smirnov test
## 
## data:  na.omit(data1$euros)
## D = 0.070339, p-value = 0.003156
## alternative hypothesis: two-sided
gamma_test(data1$euros)
## 
##  Test of fit for the Gamma distribution
## 
## data:  data1$euros
## V = 6.6554, p-value = 2.525e-06

Is gamma a correct distribution for the response variable ABUNDANCE?

summary(data1$kgs)
##     Min.  1st Qu.   Median     Mean  3rd Qu.     Max. 
##      5.9   9426.2  25451.1  29424.2  44971.4 105501.2
a=fitdist(data1$kgs, distr = "gamma",start=list(shape = 1, rate = 2), lower = -1,method = "mme")
plot(a)

ks.test(na.omit(data1$kgs), "pgamma", a$estimate[1],a$estimate[2])
## 
##  One-sample Kolmogorov-Smirnov test
## 
## data:  na.omit(data1$kgs)
## D = 0.11193, p-value = 1.606e-07
## alternative hypothesis: two-sided
gamma_test(data1$kgs)
## 
##  Test of fit for the Gamma distribution
## 
## data:  data1$kgs
## V = -9.2321, p-value = 6.661e-11

Regression models

x=as.factor(data1$Covid)
levels(x)
## [1] "after state of alarm"           "Reference after state of alarm"
## [3] "Reference No"                   "Reference state of alarm 1"    
## [5] "Reference state of alarm 2"     "state of alarm 1"              
## [7] "state of alarm 2"
data0=subset(data1,data1$Covid=="Reference state of alarm 1" | data1$Covid=="state of alarm 1")
boxplot(data0$euros~data0$Covid, col = rgb(0, 0.5, 1, alpha = 0.5),xlab="Covid Category",ylab="Price")

boxplot(data0$kgs~data0$Covid, col = rgb(0, 0.5, 1, alpha = 0.5),xlab="Covid Category",ylab="Abundance")

x=as.factor(data0$Covid)
table(x)
## x
## Reference state of alarm 1           state of alarm 1 
##                         55                         63
y=as.numeric(data0$euros)
x=relevel(x, ref = "Reference state of alarm 1")
z=as.numeric(data0$kgs)

Model0<-glm((y)~x,family=Gamma(link=log))
cooksd <- cooks.distance(Model0)
influential <- cooksd[(cooksd > (3 * mean(cooksd, na.rm = TRUE)))]
influential=as.numeric(names(influential))
y=y[-influential]
xaux=x[-influential]
Model0<-glm((y)~xaux,family=Gamma(link=log))
summary(Model0)
## 
## Call:
## glm(formula = (y) ~ xaux, family = Gamma(link = log))
## 
## Deviance Residuals: 
##      Min        1Q    Median        3Q       Max  
## -1.12717  -0.46991  -0.07522   0.23646   0.94600  
## 
## Coefficients:
##                      Estimate Std. Error t value Pr(>|t|)    
## (Intercept)           0.73457    0.07121  10.316  < 2e-16 ***
## xauxstate of alarm 1  0.27623    0.09679   2.854  0.00518 ** 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for Gamma family taken to be 0.2535242)
## 
##     Null deviance: 29.225  on 108  degrees of freedom
## Residual deviance: 27.182  on 107  degrees of freedom
## AIC: 334.02
## 
## Number of Fisher Scoring iterations: 4
Model0<-glm((z)~x,family=Gamma(link=log))
cooksd <- cooks.distance(Model0)
influential <- cooksd[(cooksd > (3 * mean(cooksd, na.rm = TRUE)))]
influential=as.numeric(names(influential))
z=z[-influential]
xaux=x[-influential]

Model0<-glm((z)~xaux,family=Gamma(link=log))
summary(Model0)
## 
## Call:
## glm(formula = (z) ~ xaux, family = Gamma(link = log))
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -3.9309  -1.0544  -0.1018   0.4568   1.3451  
## 
## Coefficients:
##                      Estimate Std. Error t value Pr(>|t|)    
## (Intercept)            9.8469     0.1126  87.474  < 2e-16 ***
## xauxstate of alarm 1   0.6538     0.1517   4.309 3.66e-05 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for Gamma family taken to be 0.6209221)
## 
##     Null deviance: 177.05  on 108  degrees of freedom
## Residual deviance: 165.96  on 107  degrees of freedom
## AIC: 2446.5
## 
## Number of Fisher Scoring iterations: 6
data01=subset(data1,data1$Covid=="Reference state of alarm 2" | data1$Covid=="state of alarm 2")
boxplot(data01$euros~data01$Covid, col = rgb(0, 0.5, 1, alpha = 0.5),xlab="Covid Category",ylab="Price")

boxplot(data01$kgs~data01$Covid, col = rgb(0, 0.5, 1, alpha = 0.5),xlab="Covid Category",ylab="Abundance")

x=as.factor(data01$Covid)
table(x)
## x
## Reference state of alarm 2           state of alarm 2 
##                         61                         79
y=as.numeric(data01$euros)
x=relevel(x, ref = "Reference state of alarm 2")
z=as.numeric(data01$kgs)


Model01<-glm((y)~x,family=Gamma(link=log))
cooksd <- cooks.distance(Model01)
influential <- cooksd[(cooksd > (3 * mean(cooksd, na.rm = TRUE)))]
influential=as.numeric(names(influential))
y=y[-influential]
xaux=x[-influential]
Model01<-glm((y)~xaux,family=Gamma(link=log))
summary(Model01)
## 
## Call:
## glm(formula = (y) ~ xaux, family = Gamma(link = log))
## 
## Deviance Residuals: 
##      Min        1Q    Median        3Q       Max  
## -0.84421  -0.45429  -0.09992   0.32447   0.89586  
## 
## Coefficients:
##                      Estimate Std. Error t value Pr(>|t|)    
## (Intercept)           1.13295    0.06118  18.519  < 2e-16 ***
## xauxstate of alarm 2 -0.37314    0.08246  -4.525 1.37e-05 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for Gamma family taken to be 0.2170772)
## 
##     Null deviance: 32.413  on 128  degrees of freedom
## Residual deviance: 27.938  on 127  degrees of freedom
## AIC: 390.76
## 
## Number of Fisher Scoring iterations: 4
Model01<-glm((z)~x,family=Gamma(link=log))
cooksd <- cooks.distance(Model01)
influential <- cooksd[(cooksd > (3 * mean(cooksd, na.rm = TRUE)))]
influential=as.numeric(names(influential))
z=z[-influential]
xaux=x[-influential]

Model01<-glm((z)~xaux,family=Gamma(link=log))
summary(Model01)
## 
## Call:
## glm(formula = (z) ~ xaux, family = Gamma(link = log))
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -3.7964  -0.7754   0.0429   0.4502   1.1324  
## 
## Coefficients:
##                      Estimate Std. Error t value Pr(>|t|)    
## (Intercept)           9.99785    0.09358 106.841   <2e-16 ***
## xauxstate of alarm 2  0.22539    0.12320   1.829   0.0697 .  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for Gamma family taken to be 0.481615)
## 
##     Null deviance: 163.13  on 129  degrees of freedom
## Residual deviance: 161.53  on 128  degrees of freedom
## AIC: 2900.7
## 
## Number of Fisher Scoring iterations: 6
data02=subset(data1,data1$Covid=="Reference after state of alarm" | data1$Covid=="after state of alarm")
boxplot(data02$euros~data02$Covid, col = rgb(0, 0.5, 1, alpha = 0.5),xlab="Covid Category",ylab="Price")

boxplot(data02$kgs~data02$Covid, col = rgb(0, 0.5, 1, alpha = 0.5),xlab="Covid Category",ylab="Abundance")

x=as.factor(data02$Covid)
table(x)
## x
##           after state of alarm Reference after state of alarm 
##                             84                             89
y=as.numeric(data02$euros)
x=relevel(x, ref = "Reference after state of alarm")
z=as.numeric(data02$kgs)

Model02<-glm((y)~x,family=Gamma(link=log))
cooksd <- cooks.distance(Model02)
influential <- cooksd[(cooksd > (3 * mean(cooksd, na.rm = TRUE)))]
influential=as.numeric(names(influential))
y=y[-influential]
xaux=x[-influential]
Model02<-glm((y)~xaux,family=Gamma(link=log))
summary(Model02)
## 
## Call:
## glm(formula = (y) ~ xaux, family = Gamma(link = log))
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -0.8348  -0.3872  -0.1519   0.2403   0.9615  
## 
## Coefficients:
##                          Estimate Std. Error t value Pr(>|t|)    
## (Intercept)               0.75413    0.04765  15.828   <2e-16 ***
## xauxafter state of alarm -0.19574    0.06759  -2.896   0.0043 ** 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for Gamma family taken to be 0.1861451)
## 
##     Null deviance: 30.127  on 162  degrees of freedom
## Residual deviance: 28.569  on 161  degrees of freedom
## AIC: 375.04
## 
## Number of Fisher Scoring iterations: 4
Model02<-glm((z)~x,family=Gamma(link=log))
cooksd <- cooks.distance(Model02)
influential <- cooksd[(cooksd > (3 * mean(cooksd, na.rm = TRUE)))]
influential=as.numeric(names(influential))
z=z[-influential]
xaux=x[-influential]

Model02<-glm((z)~xaux,family=Gamma(link=log))
summary(Model02)
## 
## Call:
## glm(formula = (z) ~ xaux, family = Gamma(link = log))
## 
## Deviance Residuals: 
##      Min        1Q    Median        3Q       Max  
## -2.79124  -0.54950  -0.02658   0.31195   0.88515  
## 
## Coefficients:
##                          Estimate Std. Error t value Pr(>|t|)    
## (Intercept)              10.17321    0.06234 163.192  < 2e-16 ***
## xauxafter state of alarm  0.53953    0.08870   6.082 8.23e-09 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for Gamma family taken to be 0.322551)
## 
##     Null deviance: 107.010  on 163  degrees of freedom
## Residual deviance:  95.194  on 162  degrees of freedom
## AIC: 3726.9
## 
## Number of Fisher Scoring iterations: 5

2.3 Sarda sarda

data1=subset(data_A,data_A$specie=="BON-BONITO O BONITO DEL SUR")

Is gamma a correct distribution for the response variable PRICE?

summary(data1$euros)
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##  0.3053  2.4311  3.2958  3.2527  3.9832  6.7572
a=fitdist(data1$euros, distr = "gamma",start=list(shape = 1, rate = 2), lower = -1,method = "mme")
plot(a)

ks.test(na.omit(data1$euros), "pgamma", a$estimate[1],a$estimate[2])
## 
##  One-sample Kolmogorov-Smirnov test
## 
## data:  na.omit(data1$euros)
## D = 0.061172, p-value = 0.02381
## alternative hypothesis: two-sided
gamma_test(data1$euros)
## 
##  Test of fit for the Gamma distribution
## 
## data:  data1$euros
## V = -8.0428, p-value = 1.292e-08

Is gamma a correct distribution for the response variable ABUNDANCE?

summary(data1$kgs)
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##    0.80   69.58  272.33  656.00  796.73 8475.38
a=fitdist(data1$kgs, distr = "gamma",start=list(shape = 1, rate = 2), lower = -1,method = "mme")
plot(a)

ks.test(na.omit(data1$kgs), "pgamma", a$estimate[1],a$estimate[2])
## 
##  One-sample Kolmogorov-Smirnov test
## 
## data:  na.omit(data1$kgs)
## D = 0.065285, p-value = 0.01287
## alternative hypothesis: two-sided
gamma_test(data1$kgs)
## 
##  Test of fit for the Gamma distribution
## 
## data:  data1$kgs
## V = 3.8754, p-value = 0.006138

Regression models

x=as.factor(data1$Covid)
levels(x)
## [1] "after state of alarm"           "Reference after state of alarm"
## [3] "Reference No"                   "Reference state of alarm 1"    
## [5] "Reference state of alarm 2"     "state of alarm 1"              
## [7] "state of alarm 2"
data0=subset(data1,data1$Covid=="Reference state of alarm 1" | data1$Covid=="state of alarm 1")
boxplot(data0$euros~data0$Covid, col = rgb(0, 0.5, 1, alpha = 0.5),xlab="Covid Category",ylab="Price")

boxplot(data0$kgs~data0$Covid, col = rgb(0, 0.5, 1, alpha = 0.5),xlab="Covid Category",ylab="Abundance")

x=as.factor(data0$Covid)
table(x)
## x
## Reference state of alarm 1           state of alarm 1 
##                         75                         62
y=as.numeric(data0$euros)
x=relevel(x, ref = "Reference state of alarm 1")
z=as.numeric(data0$kgs)

Model0<-glm((y)~x,family=Gamma(link=log))
cooksd <- cooks.distance(Model0)
influential <- cooksd[(cooksd > (3 * mean(cooksd, na.rm = TRUE)))]
influential=as.numeric(names(influential))
y=y[-influential]
xaux=x[-influential]
Model0<-glm((y)~xaux,family=Gamma(link=log))
summary(Model0)
## 
## Call:
## glm(formula = (y) ~ xaux, family = Gamma(link = log))
## 
## Deviance Residuals: 
##      Min        1Q    Median        3Q       Max  
## -0.50435  -0.20243   0.00474   0.16339   0.40782  
## 
## Coefficients:
##                      Estimate Std. Error t value Pr(>|t|)    
## (Intercept)           1.12357    0.02656  42.303   <2e-16 ***
## xauxstate of alarm 1  0.08699    0.03980   2.186   0.0307 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for Gamma family taken to be 0.05008495)
## 
##     Null deviance: 6.7928  on 127  degrees of freedom
## Residual deviance: 6.5528  on 126  degrees of freedom
## AIC: 280.91
## 
## Number of Fisher Scoring iterations: 4
Model0<-glm((z)~x,family=Gamma(link=log))
cooksd <- cooks.distance(Model0)
influential <- cooksd[(cooksd > (3 * mean(cooksd, na.rm = TRUE)))]
influential=as.numeric(names(influential))
z=z[-influential]
xaux=x[-influential]

Model0<-glm((z)~xaux,family=Gamma(link=log))
summary(Model0)
## 
## Call:
## glm(formula = (z) ~ xaux, family = Gamma(link = log))
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -3.1984  -1.1869  -0.4652   0.4436   2.1002  
## 
## Coefficients:
##                      Estimate Std. Error t value Pr(>|t|)    
## (Intercept)            5.9143     0.1295  45.669   <2e-16 ***
## xauxstate of alarm 1   0.2714     0.1907   1.423    0.157    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for Gamma family taken to be 1.157198)
## 
##     Null deviance: 229.75  on 127  degrees of freedom
## Residual deviance: 227.40  on 126  degrees of freedom
## AIC: 1796.8
## 
## Number of Fisher Scoring iterations: 7
data01=subset(data1,data1$Covid=="Reference state of alarm 2" | data1$Covid=="state of alarm 2")
boxplot(data01$euros~data01$Covid, col = rgb(0, 0.5, 1, alpha = 0.5),xlab="Covid Category",ylab="Price")

boxplot(data01$kgs~data01$Covid, col = rgb(0, 0.5, 1, alpha = 0.5),xlab="Covid Category",ylab="Abundance")

x=as.factor(data01$Covid)
table(x)
## x
## Reference state of alarm 2           state of alarm 2 
##                        100                        127
y=as.numeric(data01$euros)
x=relevel(x, ref = "Reference state of alarm 2")
z=as.numeric(data01$kgs)


Model01<-glm((y)~x,family=Gamma(link=log))
cooksd <- cooks.distance(Model01)
influential <- cooksd[(cooksd > (3 * mean(cooksd, na.rm = TRUE)))]
influential=as.numeric(names(influential))
y=y[-influential]
xaux=x[-influential]
Model01<-glm((y)~xaux,family=Gamma(link=log))
summary(Model01)
## 
## Call:
## glm(formula = (y) ~ xaux, family = Gamma(link = log))
## 
## Deviance Residuals: 
##      Min        1Q    Median        3Q       Max  
## -0.52687  -0.11627  -0.00113   0.13776   0.33368  
## 
## Coefficients:
##                      Estimate Std. Error t value Pr(>|t|)    
## (Intercept)           1.20730    0.02048  58.940  < 2e-16 ***
## xauxstate of alarm 2  0.20034    0.02651   7.558 1.36e-12 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for Gamma family taken to be 0.03482435)
## 
##     Null deviance: 9.5237  on 205  degrees of freedom
## Residual deviance: 7.5632  on 204  degrees of freedom
## AIC: 450.25
## 
## Number of Fisher Scoring iterations: 4
Model01<-glm((z)~x,family=Gamma(link=log))
cooksd <- cooks.distance(Model01)
influential <- cooksd[(cooksd > (3 * mean(cooksd, na.rm = TRUE)))]
influential=as.numeric(names(influential))
z=z[-influential]
xaux=x[-influential]

Model01<-glm((z)~xaux,family=Gamma(link=log))
summary(Model01)
## 
## Call:
## glm(formula = (z) ~ xaux, family = Gamma(link = log))
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -3.0081  -1.4172  -0.6590   0.3368   2.7458  
## 
## Coefficients:
##                      Estimate Std. Error t value Pr(>|t|)    
## (Intercept)            4.9391     0.1306  37.824  < 2e-16 ***
## xauxstate of alarm 2   1.0940     0.1750   6.251 2.25e-09 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for Gamma family taken to be 1.602857)
## 
##     Null deviance: 467.39  on 211  degrees of freedom
## Residual deviance: 409.87  on 210  degrees of freedom
## AIC: 2753
## 
## Number of Fisher Scoring iterations: 7
data02=subset(data1,data1$Covid=="Reference after state of alarm" | data1$Covid=="after state of alarm")
boxplot(data02$euros~data02$Covid, col = rgb(0, 0.5, 1, alpha = 0.5),xlab="Covid Category",ylab="Price")

boxplot(data02$kgs~data02$Covid, col = rgb(0, 0.5, 1, alpha = 0.5),xlab="Covid Category",ylab="Abundance")

x=as.factor(data02$Covid)
table(x)
## x
##           after state of alarm Reference after state of alarm 
##                             85                             94
y=as.numeric(data02$euros)
x=relevel(x, ref = "Reference after state of alarm")
z=as.numeric(data02$kgs)

Model02<-glm((y)~x,family=Gamma(link=log))
cooksd <- cooks.distance(Model02)
influential <- cooksd[(cooksd > (3 * mean(cooksd, na.rm = TRUE)))]
influential=as.numeric(names(influential))
y=y[-influential]
xaux=x[-influential]
Model02<-glm((y)~xaux,family=Gamma(link=log))
summary(Model02)
## 
## Call:
## glm(formula = (y) ~ xaux, family = Gamma(link = log))
## 
## Deviance Residuals: 
##      Min        1Q    Median        3Q       Max  
## -0.46943  -0.16882   0.02088   0.14151   0.34836  
## 
## Coefficients:
##                          Estimate Std. Error t value Pr(>|t|)    
## (Intercept)               0.72776    0.02069   35.18   <2e-16 ***
## xauxafter state of alarm  0.53980    0.02970   18.17   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for Gamma family taken to be 0.03680403)
## 
##     Null deviance: 18.5631  on 166  degrees of freedom
## Residual deviance:  6.4894  on 165  degrees of freedom
## AIC: 262.65
## 
## Number of Fisher Scoring iterations: 4
Model02<-glm((z)~x,family=Gamma(link=log))
cooksd <- cooks.distance(Model02)
influential <- cooksd[(cooksd > (3 * mean(cooksd, na.rm = TRUE)))]
influential=as.numeric(names(influential))
z=z[-influential]
xaux=x[-influential]

Model02<-glm((z)~xaux,family=Gamma(link=log))
summary(Model02)
## 
## Call:
## glm(formula = (z) ~ xaux, family = Gamma(link = log))
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -3.0324  -0.7139  -0.1901   0.3958   1.4186  
## 
## Coefficients:
##                          Estimate Std. Error t value Pr(>|t|)    
## (Intercept)               7.11085    0.08003  88.852   <2e-16 ***
## xauxafter state of alarm -1.11147    0.11605  -9.578   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for Gamma family taken to be 0.5508195)
## 
##     Null deviance: 186.00  on 163  degrees of freedom
## Residual deviance: 138.67  on 162  degrees of freedom
## AIC: 2486.8
## 
## Number of Fisher Scoring iterations: 5

2.4 Dicologlossa cuneata

data1=subset(data_A,data_A$specie=="CET-ACEDIA")

Is gamma a correct distribution for the response variable PRICE?

summary(data1$euros)
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##   3.714   7.472   9.291   9.073  10.801  15.066
a=fitdist(data1$euros, distr = "gamma",start=list(shape = 1, rate = 2), lower = -1,method = "mme")
plot(a)

ks.test(na.omit(data1$euros), "pgamma", a$estimate[1],a$estimate[2])
## 
##  One-sample Kolmogorov-Smirnov test
## 
## data:  na.omit(data1$euros)
## D = 0.084053, p-value = 0.0001886
## alternative hypothesis: two-sided
gamma_test(data1$euros)
## 
##  Test of fit for the Gamma distribution
## 
## data:  data1$euros
## V = -7.3791, p-value = 1.81e-07

Is gamma a correct distribution for the response variable ABUNDANCE?

summary(data1$kgs)
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##    0.42  195.56  389.41  648.87  731.87 6268.88
a=fitdist(data1$kgs, distr = "gamma",start=list(shape = 1, rate = 2), lower = -1,method = "mme")
plot(a)

ks.test(na.omit(data1$kgs), "pgamma", a$estimate[1],a$estimate[2])
## 
##  One-sample Kolmogorov-Smirnov test
## 
## data:  na.omit(data1$kgs)
## D = 0.13161, p-value = 2.704e-10
## alternative hypothesis: two-sided
gamma_test(data1$kgs)
## 
##  Test of fit for the Gamma distribution
## 
## data:  data1$kgs
## V = 3.8312, p-value = 0.006748

Regression models

x=as.factor(data1$Covid)
levels(x)
## [1] "after state of alarm"           "Reference after state of alarm"
## [3] "Reference No"                   "Reference state of alarm 1"    
## [5] "Reference state of alarm 2"     "state of alarm 1"              
## [7] "state of alarm 2"
data0=subset(data1,data1$Covid=="Reference state of alarm 1" | data1$Covid=="state of alarm 1")
boxplot(data0$euros~data0$Covid, col = rgb(0, 0.5, 1, alpha = 0.5),xlab="Covid Category",ylab="Price")

boxplot(data0$kgs~data0$Covid, col = rgb(0, 0.5, 1, alpha = 0.5),xlab="Covid Category",ylab="Abundance")

x=as.factor(data0$Covid)
table(x)
## x
## Reference state of alarm 1           state of alarm 1 
##                         73                         77
y=as.numeric(data0$euros)
x=relevel(x, ref = "Reference state of alarm 1")
z=as.numeric(data0$kgs)

Model0<-glm((y)~x,family=Gamma(link=log))
cooksd <- cooks.distance(Model0)
influential <- cooksd[(cooksd > (3 * mean(cooksd, na.rm = TRUE)))]
influential=as.numeric(names(influential))
y=y[-influential]
xaux=x[-influential]
Model0<-glm((y)~xaux,family=Gamma(link=log))
summary(Model0)
## 
## Call:
## glm(formula = (y) ~ xaux, family = Gamma(link = log))
## 
## Deviance Residuals: 
##      Min        1Q    Median        3Q       Max  
## -0.37900  -0.15258   0.00527   0.11726   0.28862  
## 
## Coefficients:
##                      Estimate Std. Error t value Pr(>|t|)    
## (Intercept)           2.31721    0.02107 109.954  < 2e-16 ***
## xauxstate of alarm 1 -0.13296    0.02930  -4.539  1.2e-05 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for Gamma family taken to be 0.03064504)
## 
##     Null deviance: 5.1642  on 142  degrees of freedom
## Residual deviance: 4.5324  on 141  degrees of freedom
## AIC: 557.5
## 
## Number of Fisher Scoring iterations: 4
Model0<-glm((z)~x,family=Gamma(link=log))
cooksd <- cooks.distance(Model0)
influential <- cooksd[(cooksd > (3 * mean(cooksd, na.rm = TRUE)))]
influential=as.numeric(names(influential))
z=z[-influential]
xaux=x[-influential]

Model0<-glm((z)~xaux,family=Gamma(link=log))
summary(Model0)
## 
## Call:
## glm(formula = (z) ~ xaux, family = Gamma(link = log))
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -2.9792  -0.6309  -0.0743   0.4084   1.4014  
## 
## Coefficients:
##                      Estimate Std. Error t value Pr(>|t|)    
## (Intercept)           5.38957    0.08382  64.299  < 2e-16 ***
## xauxstate of alarm 1  0.69620    0.11728   5.936 2.27e-08 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for Gamma family taken to be 0.4777618)
## 
##     Null deviance: 152.26  on 138  degrees of freedom
## Residual deviance: 135.83  on 137  degrees of freedom
## AIC: 1880.6
## 
## Number of Fisher Scoring iterations: 6
data01=subset(data1,data1$Covid=="Reference state of alarm 2" | data1$Covid=="state of alarm 2")
boxplot(data01$euros~data01$Covid, col = rgb(0, 0.5, 1, alpha = 0.5),xlab="Covid Category",ylab="Price")

boxplot(data01$kgs~data01$Covid, col = rgb(0, 0.5, 1, alpha = 0.5),xlab="Covid Category",ylab="Abundance")

x=as.factor(data01$Covid)
table(x)
## x
## Reference state of alarm 2           state of alarm 2 
##                        108                        150
y=as.numeric(data01$euros)
x=relevel(x, ref = "Reference state of alarm 2")
z=as.numeric(data01$kgs)


Model01<-glm((y)~x,family=Gamma(link=log))
cooksd <- cooks.distance(Model01)
influential <- cooksd[(cooksd > (3 * mean(cooksd, na.rm = TRUE)))]
influential=as.numeric(names(influential))
y=y[-influential]
xaux=x[-influential]
Model01<-glm((y)~xaux,family=Gamma(link=log))
summary(Model01)
## 
## Call:
## glm(formula = (y) ~ xaux, family = Gamma(link = log))
## 
## Deviance Residuals: 
##      Min        1Q    Median        3Q       Max  
## -0.56538  -0.27847  -0.02376   0.20694   0.48239  
## 
## Coefficients:
##                      Estimate Std. Error t value Pr(>|t|)    
## (Intercept)           1.93296    0.02776  69.634  < 2e-16 ***
## xauxstate of alarm 2  0.27189    0.03576   7.603 6.39e-13 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for Gamma family taken to be 0.07474317)
## 
##     Null deviance: 22.799  on 243  degrees of freedom
## Residual deviance: 18.570  on 242  degrees of freedom
## AIC: 1077.7
## 
## Number of Fisher Scoring iterations: 4
Model01<-glm((z)~x,family=Gamma(link=log))
cooksd <- cooks.distance(Model01)
influential <- cooksd[(cooksd > (3 * mean(cooksd, na.rm = TRUE)))]
influential=as.numeric(names(influential))
z=z[-influential]
xaux=x[-influential]

Model01<-glm((z)~xaux,family=Gamma(link=log))
summary(Model01)
## 
## Call:
## glm(formula = (z) ~ xaux, family = Gamma(link = log))
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -3.4916  -0.9348  -0.3173   0.5293   1.5594  
## 
## Coefficients:
##                      Estimate Std. Error t value Pr(>|t|)    
## (Intercept)           7.07225    0.08995  78.628  < 2e-16 ***
## xauxstate of alarm 2 -0.84482    0.11745  -7.193 8.42e-12 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for Gamma family taken to be 0.7928338)
## 
##     Null deviance: 445.51  on 236  degrees of freedom
## Residual deviance: 403.69  on 235  degrees of freedom
## AIC: 3581.5
## 
## Number of Fisher Scoring iterations: 6
data02=subset(data1,data1$Covid=="Reference after state of alarm" | data1$Covid=="after state of alarm")
boxplot(data02$euros~data02$Covid, col = rgb(0, 0.5, 1, alpha = 0.5),xlab="Covid Category",ylab="Price")

boxplot(data02$kgs~data02$Covid, col = rgb(0, 0.5, 1, alpha = 0.5),xlab="Covid Category",ylab="Abundance")

x=as.factor(data02$Covid)
table(x)
## x
##           after state of alarm Reference after state of alarm 
##                             95                             97
y=as.numeric(data02$euros)
x=relevel(x, ref = "Reference after state of alarm")
z=as.numeric(data02$kgs)

Model02<-glm((y)~x,family=Gamma(link=log))
cooksd <- cooks.distance(Model02)
influential <- cooksd[(cooksd > (3 * mean(cooksd, na.rm = TRUE)))]
influential=as.numeric(names(influential))
y=y[-influential]
xaux=x[-influential]
Model02<-glm((y)~xaux,family=Gamma(link=log))
summary(Model02)
## 
## Call:
## glm(formula = (y) ~ xaux, family = Gamma(link = log))
## 
## Deviance Residuals: 
##      Min        1Q    Median        3Q       Max  
## -0.33670  -0.08931  -0.00536   0.08115   0.29090  
## 
## Coefficients:
##                          Estimate Std. Error t value Pr(>|t|)    
## (Intercept)               2.25477    0.01414 159.505  < 2e-16 ***
## xauxafter state of alarm  0.05812    0.01999   2.907  0.00412 ** 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for Gamma family taken to be 0.01738498)
## 
##     Null deviance: 3.1351  on 173  degrees of freedom
## Residual deviance: 2.9881  on 172  degrees of freedom
## AIC: 584.87
## 
## Number of Fisher Scoring iterations: 4
Model02<-glm((z)~x,family=Gamma(link=log))
cooksd <- cooks.distance(Model02)
influential <- cooksd[(cooksd > (3 * mean(cooksd, na.rm = TRUE)))]
influential=as.numeric(names(influential))
z=z[-influential]
xaux=x[-influential]

Model02<-glm((z)~xaux,family=Gamma(link=log))
summary(Model02)
## 
## Call:
## glm(formula = (z) ~ xaux, family = Gamma(link = log))
## 
## Deviance Residuals: 
##      Min        1Q    Median        3Q       Max  
## -3.08712  -0.54072   0.01104   0.34074   0.93334  
## 
## Coefficients:
##                          Estimate Std. Error t value Pr(>|t|)    
## (Intercept)               5.94993    0.05944 100.106   <2e-16 ***
## xauxafter state of alarm -0.07459    0.08496  -0.878    0.381    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for Gamma family taken to be 0.3391374)
## 
##     Null deviance: 177.56  on 187  degrees of freedom
## Residual deviance: 177.30  on 186  degrees of freedom
## AIC: 2603.5
## 
## Number of Fisher Scoring iterations: 5

2.5 Parapenaeus longirostris

data1=subset(data_A,data_A$specie=="DPS-GAMBA")

Is gamma a correct distribution for the response variable PRICE?

summary(data1$euros)
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##   3.358   6.723   8.422   9.055  11.116  24.478
a=fitdist(data1$euros, distr = "gamma",start=list(shape = 1, rate = 2), lower = -1,method = "mme")
plot(a)

ks.test(na.omit(data1$euros), "pgamma", a$estimate[1],a$estimate[2])
## 
##  One-sample Kolmogorov-Smirnov test
## 
## data:  na.omit(data1$euros)
## D = 0.047178, p-value = 0.1009
## alternative hypothesis: two-sided
gamma_test(data1$euros)
## 
##  Test of fit for the Gamma distribution
## 
## data:  data1$euros
## V = 2.3248, p-value = 0.1002

Is gamma a correct distribution for the response variable ABUNDANCE?

summary(data1$kgs)
##     Min.  1st Qu.   Median     Mean  3rd Qu.     Max. 
##      2.2   4503.8   8136.0  12854.4  13267.8 117255.3
a=fitdist(data1$kgs, distr = "gamma",start=list(shape = 1, rate = 2), lower = -1,method = "mme")
plot(a)

ks.test(na.omit(data1$kgs), "pgamma", a$estimate[1],a$estimate[2])
## 
##  One-sample Kolmogorov-Smirnov test
## 
## data:  na.omit(data1$kgs)
## D = 0.14261, p-value = 2.808e-12
## alternative hypothesis: two-sided
gamma_test(data1$kgs)
## 
##  Test of fit for the Gamma distribution
## 
## data:  data1$kgs
## V = 5.3387, p-value = 0.00016

Regression models

x=as.factor(data1$Covid)
levels(x)
## [1] "after state of alarm"           "Reference after state of alarm"
## [3] "Reference No"                   "Reference state of alarm 1"    
## [5] "Reference state of alarm 2"     "state of alarm 1"              
## [7] "state of alarm 2"
data0=subset(data1,data1$Covid=="Reference state of alarm 1" | data1$Covid=="state of alarm 1")
boxplot(data0$euros~data0$Covid, col = rgb(0, 0.5, 1, alpha = 0.5),xlab="Covid Category",ylab="Price")

boxplot(data0$kgs~data0$Covid, col = rgb(0, 0.5, 1, alpha = 0.5),xlab="Covid Category",ylab="Abundance")

x=as.factor(data0$Covid)
table(x)
## x
## Reference state of alarm 1           state of alarm 1 
##                         77                         72
y=as.numeric(data0$euros)
x=relevel(x, ref = "Reference state of alarm 1")
z=as.numeric(data0$kgs)

Model0<-glm((y)~x,family=Gamma(link=log))
cooksd <- cooks.distance(Model0)
influential <- cooksd[(cooksd > (3 * mean(cooksd, na.rm = TRUE)))]
influential=as.numeric(names(influential))
y=y[-influential]
xaux=x[-influential]
Model0<-glm((y)~xaux,family=Gamma(link=log))
summary(Model0)
## 
## Call:
## glm(formula = (y) ~ xaux, family = Gamma(link = log))
## 
## Deviance Residuals: 
##      Min        1Q    Median        3Q       Max  
## -0.38462  -0.16718   0.00728   0.13680   0.33507  
## 
## Coefficients:
##                      Estimate Std. Error t value Pr(>|t|)    
## (Intercept)           1.92793    0.02195  87.818  < 2e-16 ***
## xauxstate of alarm 1  0.23271    0.03116   7.468 8.59e-12 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for Gamma family taken to be 0.03373739)
## 
##     Null deviance: 6.6556  on 138  degrees of freedom
## Residual deviance: 4.7771  on 137  degrees of freedom
## AIC: 496.04
## 
## Number of Fisher Scoring iterations: 4
Model0<-glm((z)~x,family=Gamma(link=log))
cooksd <- cooks.distance(Model0)
influential <- cooksd[(cooksd > (3 * mean(cooksd, na.rm = TRUE)))]
influential=as.numeric(names(influential))
z=z[-influential]
xaux=x[-influential]

Model0<-glm((z)~xaux,family=Gamma(link=log))
summary(Model0)
## 
## Call:
## glm(formula = (z) ~ xaux, family = Gamma(link = log))
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -2.6550  -0.4854  -0.1624   0.1694   1.8609  
## 
## Coefficients:
##                      Estimate Std. Error t value Pr(>|t|)    
## (Intercept)           9.45007    0.09191 102.816  < 2e-16 ***
## xauxstate of alarm 1 -0.54196    0.13141  -4.124 6.42e-05 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for Gamma family taken to be 0.5998028)
## 
##     Null deviance: 106.955  on 138  degrees of freedom
## Residual deviance:  96.914  on 137  degrees of freedom
## AIC: 2822.8
## 
## Number of Fisher Scoring iterations: 5
data01=subset(data1,data1$Covid=="Reference state of alarm 2" | data1$Covid=="state of alarm 2")
boxplot(data01$euros~data01$Covid, col = rgb(0, 0.5, 1, alpha = 0.5),xlab="Covid Category",ylab="Price")

boxplot(data01$kgs~data01$Covid, col = rgb(0, 0.5, 1, alpha = 0.5),xlab="Covid Category",ylab="Abundance")

x=as.factor(data01$Covid)
table(x)
## x
## Reference state of alarm 2           state of alarm 2 
##                        111                        153
y=as.numeric(data01$euros)
x=relevel(x, ref = "Reference state of alarm 2")
z=as.numeric(data01$kgs)


Model01<-glm((y)~x,family=Gamma(link=log))
cooksd <- cooks.distance(Model01)
influential <- cooksd[(cooksd > (3 * mean(cooksd, na.rm = TRUE)))]
influential=as.numeric(names(influential))
y=y[-influential]
xaux=x[-influential]
Model01<-glm((y)~xaux,family=Gamma(link=log))
summary(Model01)
## 
## Call:
## glm(formula = (y) ~ xaux, family = Gamma(link = log))
## 
## Deviance Residuals: 
##      Min        1Q    Median        3Q       Max  
## -0.60336  -0.24687  -0.04867   0.20622   0.52918  
## 
## Coefficients:
##                      Estimate Std. Error t value Pr(>|t|)    
## (Intercept)           2.15128    0.02792  77.046   <2e-16 ***
## xauxstate of alarm 2 -0.07766    0.03619  -2.146   0.0329 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for Gamma family taken to be 0.07796393)
## 
##     Null deviance: 19.509  on 246  degrees of freedom
## Residual deviance: 19.148  on 245  degrees of freedom
## AIC: 1099.3
## 
## Number of Fisher Scoring iterations: 4
Model01<-glm((z)~x,family=Gamma(link=log))
cooksd <- cooks.distance(Model01)
influential <- cooksd[(cooksd > (3 * mean(cooksd, na.rm = TRUE)))]
influential=as.numeric(names(influential))
z=z[-influential]
xaux=x[-influential]

Model01<-glm((z)~xaux,family=Gamma(link=log))
summary(Model01)
## 
## Call:
## glm(formula = (z) ~ xaux, family = Gamma(link = log))
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -3.7385  -0.6384  -0.0210   0.2668   1.8713  
## 
## Coefficients:
##                      Estimate Std. Error t value Pr(>|t|)    
## (Intercept)           9.15730    0.07504 122.037   <2e-16 ***
## xauxstate of alarm 2  0.21597    0.09786   2.207   0.0282 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for Gamma family taken to be 0.5799451)
## 
##     Null deviance: 262.50  on 249  degrees of freedom
## Residual deviance: 259.72  on 248  degrees of freedom
## AIC: 5149.5
## 
## Number of Fisher Scoring iterations: 5
data02=subset(data1,data1$Covid=="Reference after state of alarm" | data1$Covid=="after state of alarm")
boxplot(data02$euros~data02$Covid, col = rgb(0, 0.5, 1, alpha = 0.5),xlab="Covid Category",ylab="Price")

boxplot(data02$kgs~data02$Covid, col = rgb(0, 0.5, 1, alpha = 0.5),xlab="Covid Category",ylab="Abundance")

x=as.factor(data02$Covid)
table(x)
## x
##           after state of alarm Reference after state of alarm 
##                             98                            103
y=as.numeric(data02$euros)
x=relevel(x, ref = "Reference after state of alarm")
z=as.numeric(data02$kgs)

Model02<-glm((y)~x,family=Gamma(link=log))
cooksd <- cooks.distance(Model02)
influential <- cooksd[(cooksd > (3 * mean(cooksd, na.rm = TRUE)))]
influential=as.numeric(names(influential))
y=y[-influential]
xaux=x[-influential]
Model02<-glm((y)~xaux,family=Gamma(link=log))
summary(Model02)
## 
## Call:
## glm(formula = (y) ~ xaux, family = Gamma(link = log))
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -0.4591  -0.1329  -0.0033   0.1499   0.3505  
## 
## Coefficients:
##                          Estimate Std. Error t value Pr(>|t|)    
## (Intercept)               2.42856    0.02035 119.319   <2e-16 ***
## xauxafter state of alarm -0.01799    0.02878  -0.625    0.533    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for Gamma family taken to be 0.03935531)
## 
##     Null deviance: 7.7873  on 189  degrees of freedom
## Residual deviance: 7.7719  on 188  degrees of freedom
## AIC: 850.82
## 
## Number of Fisher Scoring iterations: 4
Model02<-glm((z)~x,family=Gamma(link=log))
cooksd <- cooks.distance(Model02)
influential <- cooksd[(cooksd > (3 * mean(cooksd, na.rm = TRUE)))]
influential=as.numeric(names(influential))
z=z[-influential]
xaux=x[-influential]

Model02<-glm((z)~xaux,family=Gamma(link=log))
summary(Model02)
## 
## Call:
## glm(formula = (z) ~ xaux, family = Gamma(link = log))
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -3.7536  -0.9606  -0.2257   0.0806   2.3681  
## 
## Coefficients:
##                          Estimate Std. Error t value Pr(>|t|)    
## (Intercept)               8.83277    0.11588  76.223   <2e-16 ***
## xauxafter state of alarm -0.03238    0.16524  -0.196    0.845    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for Gamma family taken to be 1.248826)
## 
##     Null deviance: 276.46  on 182  degrees of freedom
## Residual deviance: 276.41  on 181  degrees of freedom
## AIC: 3595
## 
## Number of Fisher Scoring iterations: 6

2.6 Phycis phycis

data1=subset(data_A,data_A$specie=="FOR-BROTOLA DE ROCA")

Is gamma a correct distribution for the response variable PRICE?

summary(data1$euros)
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##   0.200   4.854   5.818   5.639   6.731   9.750
a=fitdist(data1$euros, distr = "gamma",start=list(shape = 1, rate = 2), lower = -1,method = "mme")
plot(a)

ks.test(na.omit(data1$euros), "pgamma", a$estimate[1],a$estimate[2])
## 
##  One-sample Kolmogorov-Smirnov test
## 
## data:  na.omit(data1$euros)
## D = 0.10383, p-value = 7.735e-06
## alternative hypothesis: two-sided
gamma_test(data1$euros)
## 
##  Test of fit for the Gamma distribution
## 
## data:  data1$euros
## V = -18.587, p-value < 2.2e-16

Is gamma a correct distribution for the response variable ABUNDANCE?

summary(data1$kgs)
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##    0.45   16.46   56.39   83.86  123.45  425.91
a=fitdist(data1$kgs, distr = "gamma",start=list(shape = 1, rate = 2), lower = -1,method = "mme")
plot(a)

ks.test(na.omit(data1$kgs), "pgamma", a$estimate[1],a$estimate[2])
## 
##  One-sample Kolmogorov-Smirnov test
## 
## data:  na.omit(data1$kgs)
## D = 0.070609, p-value = 0.00628
## alternative hypothesis: two-sided
gamma_test(data1$kgs)
## 
##  Test of fit for the Gamma distribution
## 
## data:  data1$kgs
## V = -2.6982, p-value = 0.0564

Regression models

x=as.factor(data1$Covid)
levels(x)
## [1] "after state of alarm"           "Reference after state of alarm"
## [3] "Reference No"                   "Reference state of alarm 1"    
## [5] "Reference state of alarm 2"     "state of alarm 1"              
## [7] "state of alarm 2"
data0=subset(data1,data1$Covid=="Reference state of alarm 1" | data1$Covid=="state of alarm 1")
boxplot(data0$euros~data0$Covid, col = rgb(0, 0.5, 1, alpha = 0.5),xlab="Covid Category",ylab="Price")

boxplot(data0$kgs~data0$Covid, col = rgb(0, 0.5, 1, alpha = 0.5),xlab="Covid Category",ylab="Abundance")

x=as.factor(data0$Covid)
table(x)
## x
## Reference state of alarm 1           state of alarm 1 
##                         66                         70
y=as.numeric(data0$euros)
x=relevel(x, ref = "Reference state of alarm 1")
z=as.numeric(data0$kgs)

Model0<-glm((y)~x,family=Gamma(link=log))
cooksd <- cooks.distance(Model0)
influential <- cooksd[(cooksd > (3 * mean(cooksd, na.rm = TRUE)))]
influential=as.numeric(names(influential))
y=y[-influential]
xaux=x[-influential]
Model0<-glm((y)~xaux,family=Gamma(link=log))
summary(Model0)
## 
## Call:
## glm(formula = (y) ~ xaux, family = Gamma(link = log))
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -0.8795  -0.2747   0.0279   0.2197   0.5186  
## 
## Coefficients:
##                      Estimate Std. Error t value Pr(>|t|)    
## (Intercept)           1.72164    0.04077  42.228   <2e-16 ***
## xauxstate of alarm 1 -0.05099    0.05572  -0.915    0.362    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for Gamma family taken to be 0.09806949)
## 
##     Null deviance: 14.762  on 126  degrees of freedom
## Residual deviance: 14.680  on 125  degrees of freedom
## AIC: 510.51
## 
## Number of Fisher Scoring iterations: 4
Model0<-glm((z)~x,family=Gamma(link=log))
cooksd <- cooks.distance(Model0)
influential <- cooksd[(cooksd > (3 * mean(cooksd, na.rm = TRUE)))]
influential=as.numeric(names(influential))
z=z[-influential]
xaux=x[-influential]

Model0<-glm((z)~xaux,family=Gamma(link=log))
summary(Model0)
## 
## Call:
## glm(formula = (z) ~ xaux, family = Gamma(link = log))
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -2.5875  -0.9810  -0.2797   0.5245   1.3560  
## 
## Coefficients:
##                      Estimate Std. Error t value Pr(>|t|)    
## (Intercept)            3.9038     0.1034  37.748  < 2e-16 ***
## xauxstate of alarm 1  -0.5409     0.1463  -3.698 0.000325 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for Gamma family taken to be 0.6738042)
## 
##     Null deviance: 139.9  on 125  degrees of freedom
## Residual deviance: 130.8  on 124  degrees of freedom
## AIC: 1174.3
## 
## Number of Fisher Scoring iterations: 6
data01=subset(data1,data1$Covid=="Reference state of alarm 2" | data1$Covid=="state of alarm 2")
boxplot(data01$euros~data01$Covid, col = rgb(0, 0.5, 1, alpha = 0.5),xlab="Covid Category",ylab="Price")

boxplot(data01$kgs~data01$Covid, col = rgb(0, 0.5, 1, alpha = 0.5),xlab="Covid Category",ylab="Abundance")

x=as.factor(data01$Covid)
table(x)
## x
## Reference state of alarm 2           state of alarm 2 
##                         99                        119
y=as.numeric(data01$euros)
x=relevel(x, ref = "Reference state of alarm 2")
z=as.numeric(data01$kgs)


Model01<-glm((y)~x,family=Gamma(link=log))
cooksd <- cooks.distance(Model01)
influential <- cooksd[(cooksd > (3 * mean(cooksd, na.rm = TRUE)))]
influential=as.numeric(names(influential))
y=y[-influential]
xaux=x[-influential]
Model01<-glm((y)~xaux,family=Gamma(link=log))
summary(Model01)
## 
## Call:
## glm(formula = (y) ~ xaux, family = Gamma(link = log))
## 
## Deviance Residuals: 
##      Min        1Q    Median        3Q       Max  
## -0.50772  -0.14592   0.00829   0.13506   0.36526  
## 
## Coefficients:
##                      Estimate Std. Error t value Pr(>|t|)    
## (Intercept)           1.73450    0.02028  85.548  < 2e-16 ***
## xauxstate of alarm 2  0.11841    0.02660   4.451 1.43e-05 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for Gamma family taken to be 0.03411966)
## 
##     Null deviance: 7.8579  on 197  degrees of freedom
## Residual deviance: 7.1866  on 196  degrees of freedom
## AIC: 619.43
## 
## Number of Fisher Scoring iterations: 4
Model01<-glm((z)~x,family=Gamma(link=log))
cooksd <- cooks.distance(Model01)
influential <- cooksd[(cooksd > (3 * mean(cooksd, na.rm = TRUE)))]
influential=as.numeric(names(influential))
z=z[-influential]
xaux=x[-influential]

Model01<-glm((z)~xaux,family=Gamma(link=log))
summary(Model01)
## 
## Call:
## glm(formula = (z) ~ xaux, family = Gamma(link = log))
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -2.5987  -1.2034  -0.2790   0.5326   1.6295  
## 
## Coefficients:
##                      Estimate Std. Error t value Pr(>|t|)    
## (Intercept)           4.04443    0.09332  43.338   <2e-16 ***
## xauxstate of alarm 2  0.15691    0.12673   1.238    0.217    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for Gamma family taken to be 0.8012613)
## 
##     Null deviance: 248.39  on 200  degrees of freedom
## Residual deviance: 247.17  on 199  degrees of freedom
## AIC: 2070.5
## 
## Number of Fisher Scoring iterations: 6
data02=subset(data1,data1$Covid=="Reference after state of alarm" | data1$Covid=="after state of alarm")
boxplot(data02$euros~data02$Covid, col = rgb(0, 0.5, 1, alpha = 0.5),xlab="Covid Category",ylab="Price")

boxplot(data02$kgs~data02$Covid, col = rgb(0, 0.5, 1, alpha = 0.5),xlab="Covid Category",ylab="Abundance")

x=as.factor(data02$Covid)
table(x)
## x
##           after state of alarm Reference after state of alarm 
##                             88                             90
y=as.numeric(data02$euros)
x=relevel(x, ref = "Reference after state of alarm")
z=as.numeric(data02$kgs)

Model02<-glm((y)~x,family=Gamma(link=log))
cooksd <- cooks.distance(Model02)
influential <- cooksd[(cooksd > (3 * mean(cooksd, na.rm = TRUE)))]
influential=as.numeric(names(influential))
y=y[-influential]
xaux=x[-influential]
Model02<-glm((y)~xaux,family=Gamma(link=log))
summary(Model02)
## 
## Call:
## glm(formula = (y) ~ xaux, family = Gamma(link = log))
## 
## Deviance Residuals: 
##      Min        1Q    Median        3Q       Max  
## -0.51366  -0.09974   0.01233   0.10647   0.32859  
## 
## Coefficients:
##                          Estimate Std. Error t value Pr(>|t|)    
## (Intercept)               1.76026    0.01789  98.408   <2e-16 ***
## xauxafter state of alarm -0.05452    0.02499  -2.182   0.0306 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for Gamma family taken to be 0.02527666)
## 
##     Null deviance: 4.5498  on 161  degrees of freedom
## Residual deviance: 4.4294  on 160  degrees of freedom
## AIC: 440.23
## 
## Number of Fisher Scoring iterations: 4
Model02<-glm((z)~x,family=Gamma(link=log))
cooksd <- cooks.distance(Model02)
influential <- cooksd[(cooksd > (3 * mean(cooksd, na.rm = TRUE)))]
influential=as.numeric(names(influential))
z=z[-influential]
xaux=x[-influential]

Model02<-glm((z)~xaux,family=Gamma(link=log))
summary(Model02)
## 
## Call:
## glm(formula = (z) ~ xaux, family = Gamma(link = log))
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -2.8900  -0.7730  -0.1170   0.4571   1.0882  
## 
## Coefficients:
##                          Estimate Std. Error t value Pr(>|t|)    
## (Intercept)               4.52967    0.07936  57.081   <2e-16 ***
## xauxafter state of alarm  0.28395    0.11257   2.523   0.0126 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for Gamma family taken to be 0.5226786)
## 
##     Null deviance: 159.70  on 164  degrees of freedom
## Residual deviance: 156.38  on 163  degrees of freedom
## AIC: 1875.8
## 
## Number of Fisher Scoring iterations: 6

2.7 Auxis thazard

data1=subset(data_A,data_A$specie=="FRZ-MELVAS")

Is gamma a correct distribution for the response variable PRICE?

summary(data1$euros)
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##   0.140   1.353   1.672   1.786   2.181   4.000
a=fitdist(data1$euros, distr = "gamma",start=list(shape = 1, rate = 2), lower = -1,method = "mme")
plot(a)

ks.test(na.omit(data1$euros), "pgamma", a$estimate[1],a$estimate[2])
## 
##  One-sample Kolmogorov-Smirnov test
## 
## data:  na.omit(data1$euros)
## D = 0.058956, p-value = 0.4203
## alternative hypothesis: two-sided
gamma_test(data1$euros)
## 
##  Test of fit for the Gamma distribution
## 
## data:  data1$euros
## V = -3.1885, p-value = 0.02416

Is gamma a correct distribution for the response variable ABUNDANCE?

summary(data1$kgs)
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##    0.20    5.00   15.76  109.96   46.45 8127.56
a=fitdist(data1$kgs, distr = "gamma",start=list(shape = 1, rate = 2), lower = -1,method = "mme")
plot(a)

ks.test(na.omit(data1$kgs), "pgamma", a$estimate[1],a$estimate[2])
## 
##  One-sample Kolmogorov-Smirnov test
## 
## data:  na.omit(data1$kgs)
## D = 0.76146, p-value < 2.2e-16
## alternative hypothesis: two-sided
gamma_test(data1$kgs)
## 
##  Test of fit for the Gamma distribution
## 
## data:  data1$kgs
## V = 47.1, p-value < 2.2e-16

Regression models

x=as.factor(data1$Covid)
levels(x)
## [1] "after state of alarm"           "Reference after state of alarm"
## [3] "Reference No"                   "Reference state of alarm 1"    
## [5] "Reference state of alarm 2"     "state of alarm 1"              
## [7] "state of alarm 2"
data0=subset(data1,data1$Covid=="Reference state of alarm 1" | data1$Covid=="state of alarm 1")
boxplot(data0$euros~data0$Covid, col = rgb(0, 0.5, 1, alpha = 0.5),xlab="Covid Category",ylab="Price")

boxplot(data0$kgs~data0$Covid, col = rgb(0, 0.5, 1, alpha = 0.5),xlab="Covid Category",ylab="Abundance")

x=as.factor(data0$Covid)
table(x)
## x
## Reference state of alarm 1           state of alarm 1 
##                         33                          6
y=as.numeric(data0$euros)
x=relevel(x, ref = "Reference state of alarm 1")
z=as.numeric(data0$kgs)

Model0<-glm((y)~x,family=Gamma(link=log))
cooksd <- cooks.distance(Model0)
influential <- cooksd[(cooksd > (3 * mean(cooksd, na.rm = TRUE)))]
influential=as.numeric(names(influential))
y=y[-influential]
xaux=x[-influential]
Model0<-glm((y)~xaux,family=Gamma(link=log))
summary(Model0)
## 
## Call:
## glm(formula = (y) ~ xaux, family = Gamma(link = log))
## 
## Deviance Residuals: 
##      Min        1Q    Median        3Q       Max  
## -0.58971  -0.09963  -0.01174   0.12697   0.41650  
## 
## Coefficients:
##                      Estimate Std. Error t value Pr(>|t|)    
## (Intercept)           0.52380    0.03986  13.140 1.16e-14 ***
## xauxstate of alarm 1 -0.05020    0.09628  -0.521    0.606    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for Gamma family taken to be 0.04608106)
## 
##     Null deviance: 1.6651  on 34  degrees of freedom
## Residual deviance: 1.6527  on 33  degrees of freedom
## AIC: 33.159
## 
## Number of Fisher Scoring iterations: 4
Model0<-glm((z)~x,family=Gamma(link=log))
cooksd <- cooks.distance(Model0)
influential <- cooksd[(cooksd > (3 * mean(cooksd, na.rm = TRUE)))]
influential=as.numeric(names(influential))
z=z[-influential]
xaux=x[-influential]

Model0<-glm((z)~xaux,family=Gamma(link=log))
summary(Model0)
## 
## Call:
## glm(formula = (z) ~ xaux, family = Gamma(link = log))
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -2.4622  -1.5110  -0.9486   0.4156   2.9786  
## 
## Coefficients:
##                      Estimate Std. Error t value Pr(>|t|)    
## (Intercept)            4.4051     0.2702  16.302   <2e-16 ***
## xauxstate of alarm 1  -1.7322     0.7351  -2.356   0.0242 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for Gamma family taken to be 2.336662)
## 
##     Null deviance: 79.287  on 36  degrees of freedom
## Residual deviance: 70.691  on 35  degrees of freedom
## AIC: 383.82
## 
## Number of Fisher Scoring iterations: 7
data01=subset(data1,data1$Covid=="Reference state of alarm 2" | data1$Covid=="state of alarm 2")
boxplot(data01$euros~data01$Covid, col = rgb(0, 0.5, 1, alpha = 0.5),xlab="Covid Category",ylab="Price")

boxplot(data01$kgs~data01$Covid, col = rgb(0, 0.5, 1, alpha = 0.5),xlab="Covid Category",ylab="Abundance")

x=as.factor(data01$Covid)
table(x)
## x
## Reference state of alarm 2           state of alarm 2 
##                         27                         31
y=as.numeric(data01$euros)
x=relevel(x, ref = "Reference state of alarm 2")
z=as.numeric(data01$kgs)


Model01<-glm((y)~x,family=Gamma(link=log))
cooksd <- cooks.distance(Model01)
influential <- cooksd[(cooksd > (3 * mean(cooksd, na.rm = TRUE)))]
influential=as.numeric(names(influential))
y=y[-influential]
xaux=x[-influential]
Model01<-glm((y)~xaux,family=Gamma(link=log))
summary(Model01)
## 
## Call:
## glm(formula = (y) ~ xaux, family = Gamma(link = log))
## 
## Deviance Residuals: 
##      Min        1Q    Median        3Q       Max  
## -0.84798  -0.14365  -0.02223   0.12282   0.56529  
## 
## Coefficients:
##                      Estimate Std. Error t value Pr(>|t|)    
## (Intercept)           0.61718    0.05488  11.247 4.71e-15 ***
## xauxstate of alarm 2 -0.09382    0.07761  -1.209    0.233    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for Gamma family taken to be 0.0752833)
## 
##     Null deviance: 3.824  on 49  degrees of freedom
## Residual deviance: 3.714  on 48  degrees of freedom
## AIC: 71.83
## 
## Number of Fisher Scoring iterations: 4
Model01<-glm((z)~x,family=Gamma(link=log))
cooksd <- cooks.distance(Model01)
influential <- cooksd[(cooksd > (3 * mean(cooksd, na.rm = TRUE)))]
influential=as.numeric(names(influential))
z=z[-influential]
xaux=x[-influential]

Model01<-glm((z)~xaux,family=Gamma(link=log))
summary(Model01)
## 
## Call:
## glm(formula = (z) ~ xaux, family = Gamma(link = log))
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -2.1040  -1.4227  -0.9605  -0.1901   3.2759  
## 
## Coefficients:
##                      Estimate Std. Error t value Pr(>|t|)    
## (Intercept)            2.2897     0.3141   7.290  1.7e-09 ***
## xauxstate of alarm 2   0.6832     0.4286   1.594    0.117    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for Gamma family taken to be 2.466083)
## 
##     Null deviance: 101.241  on 53  degrees of freedom
## Residual deviance:  95.192  on 52  degrees of freedom
## AIC: 396.39
## 
## Number of Fisher Scoring iterations: 6
data02=subset(data1,data1$Covid=="Reference after state of alarm" | data1$Covid=="after state of alarm")
boxplot(data02$euros~data02$Covid, col = rgb(0, 0.5, 1, alpha = 0.5),xlab="Covid Category",ylab="Price")

boxplot(data02$kgs~data02$Covid, col = rgb(0, 0.5, 1, alpha = 0.5),xlab="Covid Category",ylab="Abundance")

x=as.factor(data02$Covid)
table(x)
## x
##           after state of alarm Reference after state of alarm 
##                             52                             54
y=as.numeric(data02$euros)
x=relevel(x, ref = "Reference after state of alarm")
z=as.numeric(data02$kgs)

Model02<-glm((y)~x,family=Gamma(link=log))
cooksd <- cooks.distance(Model02)
influential <- cooksd[(cooksd > (3 * mean(cooksd, na.rm = TRUE)))]
influential=as.numeric(names(influential))
y=y[-influential]
xaux=x[-influential]
Model02<-glm((y)~xaux,family=Gamma(link=log))
summary(Model02)
## 
## Call:
## glm(formula = (y) ~ xaux, family = Gamma(link = log))
## 
## Deviance Residuals: 
##      Min        1Q    Median        3Q       Max  
## -0.96473  -0.28649  -0.04483   0.23217   0.67922  
## 
## Coefficients:
##                          Estimate Std. Error t value Pr(>|t|)    
## (Intercept)               0.36050    0.05035   7.160 1.85e-10 ***
## xauxafter state of alarm  0.27679    0.07083   3.908 0.000177 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for Gamma family taken to be 0.119149)
## 
##     Null deviance: 13.841  on 94  degrees of freedom
## Residual deviance: 12.029  on 93  degrees of freedom
## AIC: 164.32
## 
## Number of Fisher Scoring iterations: 4
Model02<-glm((z)~x,family=Gamma(link=log))
cooksd <- cooks.distance(Model02)
influential <- cooksd[(cooksd > (3 * mean(cooksd, na.rm = TRUE)))]
influential=as.numeric(names(influential))
z=z[-influential]
xaux=x[-influential]

Model02<-glm((z)~xaux,family=Gamma(link=log))
summary(Model02)
## 
## Call:
## glm(formula = (z) ~ xaux, family = Gamma(link = log))
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -2.9346  -1.4236  -0.7563  -0.0906   3.3765  
## 
## Coefficients:
##                          Estimate Std. Error t value Pr(>|t|)    
## (Intercept)                3.6916     0.2167  17.037   <2e-16 ***
## xauxafter state of alarm   0.2145     0.3094   0.693     0.49    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for Gamma family taken to be 2.488435)
## 
##     Null deviance: 199.35  on 103  degrees of freedom
## Residual deviance: 198.15  on 102  degrees of freedom
## AIC: 990.33
## 
## Number of Fisher Scoring iterations: 6

2.8 Euthynnus alletteratus

data1=subset(data_A,data_A$specie=="LTA-BACORETA")

Is gamma a correct distribution for the response variable PRICE?

summary(data1$euros)
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##  0.5028  1.9003  2.4780  2.5790  3.0463  6.5008
a=fitdist(data1$euros, distr = "gamma",start=list(shape = 1, rate = 2), lower = -1,method = "mme")
plot(a)

ks.test(na.omit(data1$euros), "pgamma", a$estimate[1],a$estimate[2])
## 
##  One-sample Kolmogorov-Smirnov test
## 
## data:  na.omit(data1$euros)
## D = 0.04154, p-value = 0.4512
## alternative hypothesis: two-sided
gamma_test(data1$euros)
## 
##  Test of fit for the Gamma distribution
## 
## data:  data1$euros
## V = -0.7655, p-value = 0.5883

Is gamma a correct distribution for the response variable ABUNDANCE?

summary(data1$kgs)
##     Min.  1st Qu.   Median     Mean  3rd Qu.     Max. 
##    0.550    8.412   35.645   97.825  117.360 1265.020
a=fitdist(data1$kgs, distr = "gamma",start=list(shape = 1, rate = 2), lower = -1,method = "mme")
plot(a)

ks.test(na.omit(data1$kgs), "pgamma", a$estimate[1],a$estimate[2])
## 
##  One-sample Kolmogorov-Smirnov test
## 
## data:  na.omit(data1$kgs)
## D = 0.16334, p-value = 2.415e-10
## alternative hypothesis: two-sided
gamma_test(data1$kgs)
## 
##  Test of fit for the Gamma distribution
## 
## data:  data1$kgs
## V = 6.9045, p-value = 1.049e-06

Regression models

x=as.factor(data1$Covid)
levels(x)
## [1] "after state of alarm"           "Reference after state of alarm"
## [3] "Reference No"                   "Reference state of alarm 1"    
## [5] "Reference state of alarm 2"     "state of alarm 1"              
## [7] "state of alarm 2"
data0=subset(data1,data1$Covid=="Reference state of alarm 1" | data1$Covid=="state of alarm 1")
boxplot(data0$euros~data0$Covid, col = rgb(0, 0.5, 1, alpha = 0.5),xlab="Covid Category",ylab="Price")

boxplot(data0$kgs~data0$Covid, col = rgb(0, 0.5, 1, alpha = 0.5),xlab="Covid Category",ylab="Abundance")

x=as.factor(data0$Covid)
table(x)
## x
## Reference state of alarm 1           state of alarm 1 
##                         64                         49
y=as.numeric(data0$euros)
x=relevel(x, ref = "Reference state of alarm 1")
z=as.numeric(data0$kgs)

Model0<-glm((y)~x,family=Gamma(link=log))
cooksd <- cooks.distance(Model0)
influential <- cooksd[(cooksd > (3 * mean(cooksd, na.rm = TRUE)))]
influential=as.numeric(names(influential))
y=y[-influential]
xaux=x[-influential]
Model0<-glm((y)~xaux,family=Gamma(link=log))
summary(Model0)
## 
## Call:
## glm(formula = (y) ~ xaux, family = Gamma(link = log))
## 
## Deviance Residuals: 
##      Min        1Q    Median        3Q       Max  
## -0.70107  -0.17661  -0.03767   0.14335   0.53732  
## 
## Coefficients:
##                      Estimate Std. Error t value Pr(>|t|)    
## (Intercept)           1.02460    0.03108   32.97   <2e-16 ***
## xauxstate of alarm 1 -0.11354    0.04810   -2.36   0.0202 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for Gamma family taken to be 0.05795822)
## 
##     Null deviance: 6.2585  on 102  degrees of freedom
## Residual deviance: 5.9377  on 101  degrees of freedom
## AIC: 200.76
## 
## Number of Fisher Scoring iterations: 4
Model0<-glm((z)~x,family=Gamma(link=log))
cooksd <- cooks.distance(Model0)
influential <- cooksd[(cooksd > (3 * mean(cooksd, na.rm = TRUE)))]
influential=as.numeric(names(influential))
z=z[-influential]
xaux=x[-influential]

Model0<-glm((z)~xaux,family=Gamma(link=log))
summary(Model0)
## 
## Call:
## glm(formula = (z) ~ xaux, family = Gamma(link = log))
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -2.6834  -1.5327  -0.3941   0.5068   2.0814  
## 
## Coefficients:
##                      Estimate Std. Error t value Pr(>|t|)    
## (Intercept)            4.3030     0.1358  31.696   <2e-16 ***
## xauxstate of alarm 1   0.3641     0.2061   1.767   0.0802 .  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for Gamma family taken to be 1.105823)
## 
##     Null deviance: 187.50  on 105  degrees of freedom
## Residual deviance: 184.01  on 104  degrees of freedom
## AIC: 1155.7
## 
## Number of Fisher Scoring iterations: 7
data01=subset(data1,data1$Covid=="Reference state of alarm 2" | data1$Covid=="state of alarm 2")
boxplot(data01$euros~data01$Covid, col = rgb(0, 0.5, 1, alpha = 0.5),xlab="Covid Category",ylab="Price")

boxplot(data01$kgs~data01$Covid, col = rgb(0, 0.5, 1, alpha = 0.5),xlab="Covid Category",ylab="Abundance")

x=as.factor(data01$Covid)
table(x)
## x
## Reference state of alarm 2           state of alarm 2 
##                         54                         63
y=as.numeric(data01$euros)
x=relevel(x, ref = "Reference state of alarm 2")
z=as.numeric(data01$kgs)


Model01<-glm((y)~x,family=Gamma(link=log))
cooksd <- cooks.distance(Model01)
influential <- cooksd[(cooksd > (3 * mean(cooksd, na.rm = TRUE)))]
influential=as.numeric(names(influential))
y=y[-influential]
xaux=x[-influential]
Model01<-glm((y)~xaux,family=Gamma(link=log))
summary(Model01)
## 
## Call:
## glm(formula = (y) ~ xaux, family = Gamma(link = log))
## 
## Deviance Residuals: 
##      Min        1Q    Median        3Q       Max  
## -0.95149  -0.30241  -0.00117   0.17446   0.64276  
## 
## Coefficients:
##                        Estimate Std. Error t value Pr(>|t|)    
## (Intercept)           8.785e-01  4.932e-02  17.814   <2e-16 ***
## xauxstate of alarm 2 -4.638e-05  6.849e-02  -0.001    0.999    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for Gamma family taken to be 0.1264668)
## 
##     Null deviance: 14.421  on 107  degrees of freedom
## Residual deviance: 14.421  on 106  degrees of freedom
## AIC: 272.78
## 
## Number of Fisher Scoring iterations: 4
Model01<-glm((z)~x,family=Gamma(link=log))
cooksd <- cooks.distance(Model01)
influential <- cooksd[(cooksd > (3 * mean(cooksd, na.rm = TRUE)))]
influential=as.numeric(names(influential))
z=z[-influential]
xaux=x[-influential]

Model01<-glm((z)~xaux,family=Gamma(link=log))
summary(Model01)
## 
## Call:
## glm(formula = (z) ~ xaux, family = Gamma(link = log))
## 
## Deviance Residuals: 
##    Min      1Q  Median      3Q     Max  
## -2.205  -1.542  -0.686   0.347   2.470  
## 
## Coefficients:
##                      Estimate Std. Error t value Pr(>|t|)    
## (Intercept)            3.4730     0.1813  19.156   <2e-16 ***
## xauxstate of alarm 2  -0.1470     0.2497  -0.589    0.557    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for Gamma family taken to be 1.709321)
## 
##     Null deviance: 182.36  on 109  degrees of freedom
## Residual deviance: 181.76  on 108  degrees of freedom
## AIC: 967.12
## 
## Number of Fisher Scoring iterations: 6
data02=subset(data1,data1$Covid=="Reference after state of alarm" | data1$Covid=="after state of alarm")
boxplot(data02$euros~data02$Covid, col = rgb(0, 0.5, 1, alpha = 0.5),xlab="Covid Category",ylab="Price")

boxplot(data02$kgs~data02$Covid, col = rgb(0, 0.5, 1, alpha = 0.5),xlab="Covid Category",ylab="Abundance")

x=as.factor(data02$Covid)
table(x)
## x
##           after state of alarm Reference after state of alarm 
##                             72                             87
y=as.numeric(data02$euros)
x=relevel(x, ref = "Reference after state of alarm")
z=as.numeric(data02$kgs)

Model02<-glm((y)~x,family=Gamma(link=log))
cooksd <- cooks.distance(Model02)
influential <- cooksd[(cooksd > (3 * mean(cooksd, na.rm = TRUE)))]
influential=as.numeric(names(influential))
y=y[-influential]
xaux=x[-influential]
Model02<-glm((y)~xaux,family=Gamma(link=log))
summary(Model02)
## 
## Call:
## glm(formula = (y) ~ xaux, family = Gamma(link = log))
## 
## Deviance Residuals: 
##      Min        1Q    Median        3Q       Max  
## -0.74291  -0.21430  -0.00723   0.17032   0.57490  
## 
## Coefficients:
##                          Estimate Std. Error t value Pr(>|t|)    
## (Intercept)               0.68901    0.03318  20.764  < 2e-16 ***
## xauxafter state of alarm  0.31234    0.04882   6.398  2.1e-09 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for Gamma family taken to be 0.08588568)
## 
##     Null deviance: 16.843  on 144  degrees of freedom
## Residual deviance: 13.313  on 143  degrees of freedom
## AIC: 301.81
## 
## Number of Fisher Scoring iterations: 4
Model02<-glm((z)~x,family=Gamma(link=log))
cooksd <- cooks.distance(Model02)
influential <- cooksd[(cooksd > (3 * mean(cooksd, na.rm = TRUE)))]
influential=as.numeric(names(influential))
z=z[-influential]
xaux=x[-influential]

Model02<-glm((z)~xaux,family=Gamma(link=log))
summary(Model02)
## 
## Call:
## glm(formula = (z) ~ xaux, family = Gamma(link = log))
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -2.5762  -1.2827  -0.4057   0.3879   2.3869  
## 
## Coefficients:
##                          Estimate Std. Error t value Pr(>|t|)    
## (Intercept)                4.6765     0.1197  39.064  < 2e-16 ***
## xauxafter state of alarm  -0.7915     0.1800  -4.397 2.11e-05 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for Gamma family taken to be 1.175181)
## 
##     Null deviance: 225.81  on 146  degrees of freedom
## Residual deviance: 204.29  on 145  degrees of freedom
## AIC: 1571.7
## 
## Number of Fisher Scoring iterations: 6

2.9 Nephrops norvegicus

data1=subset(data_A,data_A$specie=="NEP-CIGALA")

Is gamma a correct distribution for the response variable PRICE?

summary(data1$euros)
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##   6.748  13.950  16.773  18.268  21.018  50.387
a=fitdist(data1$euros, distr = "gamma",start=list(shape = 1, rate = 2), lower = -1,method = "mme")
plot(a)

ks.test(na.omit(data1$euros), "pgamma", a$estimate[1],a$estimate[2])
## 
##  One-sample Kolmogorov-Smirnov test
## 
## data:  na.omit(data1$euros)
## D = 0.078144, p-value = 0.001135
## alternative hypothesis: two-sided
gamma_test(data1$euros)
## 
##  Test of fit for the Gamma distribution
## 
## data:  data1$euros
## V = 10.013, p-value = 1.438e-12

Is gamma a correct distribution for the response variable ABUNDANCE?

summary(data1$kgs)
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##    1.45  257.78  611.47  763.47 1158.36 3528.61
a=fitdist(data1$kgs, distr = "gamma",start=list(shape = 1, rate = 2), lower = -1,method = "mme")
plot(a)

ks.test(na.omit(data1$kgs), "pgamma", a$estimate[1],a$estimate[2])
## 
##  One-sample Kolmogorov-Smirnov test
## 
## data:  na.omit(data1$kgs)
## D = 0.070532, p-value = 0.004535
## alternative hypothesis: two-sided
gamma_test(data1$kgs)
## 
##  Test of fit for the Gamma distribution
## 
## data:  data1$kgs
## V = -6.6211, p-value = 2.843e-06

Regression models

x=as.factor(data1$Covid)
levels(x)
## [1] "after state of alarm"           "Reference after state of alarm"
## [3] "Reference No"                   "Reference state of alarm 1"    
## [5] "Reference state of alarm 2"     "state of alarm 1"              
## [7] "state of alarm 2"
data0=subset(data1,data1$Covid=="Reference state of alarm 1" | data1$Covid=="state of alarm 1")
boxplot(data0$euros~data0$Covid, col = rgb(0, 0.5, 1, alpha = 0.5),xlab="Covid Category",ylab="Price")

boxplot(data0$kgs~data0$Covid, col = rgb(0, 0.5, 1, alpha = 0.5),xlab="Covid Category",ylab="Abundance")

x=as.factor(data0$Covid)
table(x)
## x
## Reference state of alarm 1           state of alarm 1 
##                         73                         67
y=as.numeric(data0$euros)
x=relevel(x, ref = "Reference state of alarm 1")
z=as.numeric(data0$kgs)

Model0<-glm((y)~x,family=Gamma(link=log))
cooksd <- cooks.distance(Model0)
influential <- cooksd[(cooksd > (3 * mean(cooksd, na.rm = TRUE)))]
influential=as.numeric(names(influential))
y=y[-influential]
xaux=x[-influential]
Model0<-glm((y)~xaux,family=Gamma(link=log))
summary(Model0)
## 
## Call:
## glm(formula = (y) ~ xaux, family = Gamma(link = log))
## 
## Deviance Residuals: 
##       Min         1Q     Median         3Q        Max  
## -0.307910  -0.117928  -0.000581   0.093698   0.284494  
## 
## Coefficients:
##                       Estimate Std. Error t value Pr(>|t|)    
## (Intercept)           2.567204   0.017579 146.035   <2e-16 ***
## xauxstate of alarm 1 -0.003184   0.025455  -0.125    0.901    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for Gamma family taken to be 0.02101445)
## 
##     Null deviance: 2.7300  on 129  degrees of freedom
## Residual deviance: 2.7297  on 128  degrees of freedom
## AIC: 537.49
## 
## Number of Fisher Scoring iterations: 4
Model0<-glm((z)~x,family=Gamma(link=log))
cooksd <- cooks.distance(Model0)
influential <- cooksd[(cooksd > (3 * mean(cooksd, na.rm = TRUE)))]
influential=as.numeric(names(influential))
z=z[-influential]
xaux=x[-influential]

Model0<-glm((z)~xaux,family=Gamma(link=log))
summary(Model0)
## 
## Call:
## glm(formula = (z) ~ xaux, family = Gamma(link = log))
## 
## Deviance Residuals: 
##      Min        1Q    Median        3Q       Max  
## -2.95603  -0.67846  -0.06629   0.46635   1.09301  
## 
## Coefficients:
##                      Estimate Std. Error t value Pr(>|t|)    
## (Intercept)           6.87402    0.07897  87.048   <2e-16 ***
## xauxstate of alarm 1 -0.08653    0.11479  -0.754    0.452    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for Gamma family taken to be 0.4302848)
## 
##     Null deviance: 127.92  on 130  degrees of freedom
## Residual deviance: 127.67  on 129  degrees of freedom
## AIC: 2057.7
## 
## Number of Fisher Scoring iterations: 6
data01=subset(data1,data1$Covid=="Reference state of alarm 2" | data1$Covid=="state of alarm 2")
boxplot(data01$euros~data01$Covid, col = rgb(0, 0.5, 1, alpha = 0.5),xlab="Covid Category",ylab="Price")

boxplot(data01$kgs~data01$Covid, col = rgb(0, 0.5, 1, alpha = 0.5),xlab="Covid Category",ylab="Abundance")

x=as.factor(data01$Covid)
table(x)
## x
## Reference state of alarm 2           state of alarm 2 
##                        102                        134
y=as.numeric(data01$euros)
x=relevel(x, ref = "Reference state of alarm 2")
z=as.numeric(data01$kgs)


Model01<-glm((y)~x,family=Gamma(link=log))
cooksd <- cooks.distance(Model01)
influential <- cooksd[(cooksd > (3 * mean(cooksd, na.rm = TRUE)))]
influential=as.numeric(names(influential))
y=y[-influential]
xaux=x[-influential]
Model01<-glm((y)~xaux,family=Gamma(link=log))
summary(Model01)
## 
## Call:
## glm(formula = (y) ~ xaux, family = Gamma(link = log))
## 
## Deviance Residuals: 
##      Min        1Q    Median        3Q       Max  
## -0.61511  -0.24369  -0.01318   0.16850   0.52518  
## 
## Coefficients:
##                      Estimate Std. Error t value Pr(>|t|)    
## (Intercept)           2.93221    0.02681  109.36  < 2e-16 ***
## xauxstate of alarm 2  0.21602    0.03553    6.08 5.23e-09 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for Gamma family taken to be 0.06902046)
## 
##     Null deviance: 17.995  on 222  degrees of freedom
## Residual deviance: 15.474  on 221  degrees of freedom
## AIC: 1393.6
## 
## Number of Fisher Scoring iterations: 4
Model01<-glm((z)~x,family=Gamma(link=log))
cooksd <- cooks.distance(Model01)
influential <- cooksd[(cooksd > (3 * mean(cooksd, na.rm = TRUE)))]
influential=as.numeric(names(influential))
z=z[-influential]
xaux=x[-influential]

Model01<-glm((z)~xaux,family=Gamma(link=log))
summary(Model01)
## 
## Call:
## glm(formula = (z) ~ xaux, family = Gamma(link = log))
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -3.0118  -0.7870  -0.1432   0.3485   1.5700  
## 
## Coefficients:
##                      Estimate Std. Error t value Pr(>|t|)    
## (Intercept)           6.14155    0.07698  79.783   <2e-16 ***
## xauxstate of alarm 2 -0.23852    0.10309  -2.314   0.0216 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for Gamma family taken to be 0.5688643)
## 
##     Null deviance: 208.75  on 216  degrees of freedom
## Residual deviance: 205.69  on 215  degrees of freedom
## AIC: 3045.7
## 
## Number of Fisher Scoring iterations: 5
data02=subset(data1,data1$Covid=="Reference after state of alarm" | data1$Covid=="after state of alarm")
boxplot(data02$euros~data02$Covid, col = rgb(0, 0.5, 1, alpha = 0.5),xlab="Covid Category",ylab="Price")

boxplot(data02$kgs~data02$Covid, col = rgb(0, 0.5, 1, alpha = 0.5),xlab="Covid Category",ylab="Abundance")

x=as.factor(data02$Covid)
table(x)
## x
##           after state of alarm Reference after state of alarm 
##                             92                             88
y=as.numeric(data02$euros)
x=relevel(x, ref = "Reference after state of alarm")
z=as.numeric(data02$kgs)

Model02<-glm((y)~x,family=Gamma(link=log))
cooksd <- cooks.distance(Model02)
influential <- cooksd[(cooksd > (3 * mean(cooksd, na.rm = TRUE)))]
influential=as.numeric(names(influential))
y=y[-influential]
xaux=x[-influential]
Model02<-glm((y)~xaux,family=Gamma(link=log))
summary(Model02)
## 
## Call:
## glm(formula = (y) ~ xaux, family = Gamma(link = log))
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -0.3834  -0.1385  -0.0191   0.1197   0.3271  
## 
## Coefficients:
##                          Estimate Std. Error t value Pr(>|t|)    
## (Intercept)                2.7664     0.0184 150.326  < 2e-16 ***
## xauxafter state of alarm   0.1091     0.0258   4.227 3.85e-05 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for Gamma family taken to be 0.02878513)
## 
##     Null deviance: 5.4599  on 172  degrees of freedom
## Residual deviance: 4.9462  on 171  degrees of freedom
## AIC: 854.23
## 
## Number of Fisher Scoring iterations: 4
Model02<-glm((z)~x,family=Gamma(link=log))
cooksd <- cooks.distance(Model02)
influential <- cooksd[(cooksd > (3 * mean(cooksd, na.rm = TRUE)))]
influential=as.numeric(names(influential))
z=z[-influential]
xaux=x[-influential]

Model02<-glm((z)~xaux,family=Gamma(link=log))
summary(Model02)
## 
## Call:
## glm(formula = (z) ~ xaux, family = Gamma(link = log))
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -3.1828  -0.5732  -0.0162   0.4175   0.9336  
## 
## Coefficients:
##                          Estimate Std. Error t value Pr(>|t|)    
## (Intercept)               6.91307    0.06375  108.45   <2e-16 ***
## xauxafter state of alarm -0.13715    0.08963   -1.53    0.128    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for Gamma family taken to be 0.3454119)
## 
##     Null deviance: 116.00  on 171  degrees of freedom
## Residual deviance: 115.19  on 170  degrees of freedom
## AIC: 2683.1
## 
## Number of Fisher Scoring iterations: 5

2.10 Scorpaena scrofa

data1=subset(data_A,data_A$specie=="RSE-CABRACHO" )

Is gamma a correct distribution for the response variable PRICE?

summary(data1$euros)
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##    5.80   16.23   20.01   19.00   22.00   27.30
a=fitdist(data1$euros, distr = "gamma",start=list(shape = 1, rate = 2), lower = -1,method = "mme")
plot(a)

ks.test(na.omit(data1$euros), "pgamma", a$estimate[1],a$estimate[2])
## 
##  One-sample Kolmogorov-Smirnov test
## 
## data:  na.omit(data1$euros)
## D = 0.14208, p-value = 4.356e-08
## alternative hypothesis: two-sided
gamma_test(data1$euros)
## 
##  Test of fit for the Gamma distribution
## 
## data:  data1$euros
## V = -12.305, p-value < 2.2e-16

Is gamma a correct distribution for the response variable ABUNDANCE?

summary(data1$kgs)
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##   0.300   2.350   6.550   9.243  13.100  47.300
a=fitdist(data1$kgs, distr = "gamma",start=list(shape = 1, rate = 2), lower = -1,method = "mme")
plot(a)

ks.test(na.omit(data1$kgs), "pgamma", a$estimate[1],a$estimate[2])
## 
##  One-sample Kolmogorov-Smirnov test
## 
## data:  na.omit(data1$kgs)
## D = 0.048134, p-value = 0.2634
## alternative hypothesis: two-sided
gamma_test(data1$kgs)
## 
##  Test of fit for the Gamma distribution
## 
## data:  data1$kgs
## V = -0.15861, p-value = 0.9107

Regression models

x=as.factor(data1$Covid)
levels(x)
## [1] "after state of alarm"           "Reference after state of alarm"
## [3] "Reference No"                   "Reference state of alarm 1"    
## [5] "Reference state of alarm 2"     "state of alarm 1"              
## [7] "state of alarm 2"
data0=subset(data1,data1$Covid=="Reference state of alarm 1" | data1$Covid=="state of alarm 1")
boxplot(data0$euros~data0$Covid, col = rgb(0, 0.5, 1, alpha = 0.5),xlab="Covid Category",ylab="Price")

boxplot(data0$kgs~data0$Covid, col = rgb(0, 0.5, 1, alpha = 0.5),xlab="Covid Category",ylab="Abundance")

x=as.factor(data0$Covid)
table(x)
## x
## Reference state of alarm 1           state of alarm 1 
##                         54                         38
y=as.numeric(data0$euros)
x=relevel(x, ref = "Reference state of alarm 1")
z=as.numeric(data0$kgs)

Model0<-glm((y)~x,family=Gamma(link=log))
cooksd <- cooks.distance(Model0)
influential <- cooksd[(cooksd > (3 * mean(cooksd, na.rm = TRUE)))]
influential=as.numeric(names(influential))
y=y[-influential]
xaux=x[-influential]
Model0<-glm((y)~xaux,family=Gamma(link=log))
summary(Model0)
## 
## Call:
## glm(formula = (y) ~ xaux, family = Gamma(link = log))
## 
## Deviance Residuals: 
##      Min        1Q    Median        3Q       Max  
## -0.44191  -0.11317   0.02065   0.11632   0.25592  
## 
## Coefficients:
##                      Estimate Std. Error t value Pr(>|t|)    
## (Intercept)           2.94510    0.02440 120.700  < 2e-16 ***
## xauxstate of alarm 1 -0.20279    0.03858  -5.256 1.12e-06 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for Gamma family taken to be 0.030364)
## 
##     Null deviance: 3.5404  on 84  degrees of freedom
## Residual deviance: 2.7140  on 83  degrees of freedom
## AIC: 439.08
## 
## Number of Fisher Scoring iterations: 4
Model0<-glm((z)~x,family=Gamma(link=log))
cooksd <- cooks.distance(Model0)
influential <- cooksd[(cooksd > (3 * mean(cooksd, na.rm = TRUE)))]
influential=as.numeric(names(influential))
z=z[-influential]
xaux=x[-influential]

Model0<-glm((z)~xaux,family=Gamma(link=log))
summary(Model0)
## 
## Call:
## glm(formula = (z) ~ xaux, family = Gamma(link = log))
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -2.0311  -0.8710  -0.2080   0.4706   1.3760  
## 
## Coefficients:
##                      Estimate Std. Error t value Pr(>|t|)    
## (Intercept)            1.9638     0.1118  17.558   <2e-16 ***
## xauxstate of alarm 1  -0.2943     0.1729  -1.702   0.0924 .  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for Gamma family taken to be 0.6254469)
## 
##     Null deviance: 75.439  on 85  degrees of freedom
## Residual deviance: 73.661  on 84  degrees of freedom
## AIC: 491.68
## 
## Number of Fisher Scoring iterations: 5
data01=subset(data1,data1$Covid=="Reference state of alarm 2" | data1$Covid=="state of alarm 2")
boxplot(data01$euros~data01$Covid, col = rgb(0, 0.5, 1, alpha = 0.5),xlab="Covid Category",ylab="Price")

boxplot(data01$kgs~data01$Covid, col = rgb(0, 0.5, 1, alpha = 0.5),xlab="Covid Category",ylab="Abundance")

x=as.factor(data01$Covid)
table(x)
## x
## Reference state of alarm 2           state of alarm 2 
##                         74                         89
y=as.numeric(data01$euros)
x=relevel(x, ref = "Reference state of alarm 2")
z=as.numeric(data01$kgs)


Model01<-glm((y)~x,family=Gamma(link=log))
cooksd <- cooks.distance(Model01)
influential <- cooksd[(cooksd > (3 * mean(cooksd, na.rm = TRUE)))]
influential=as.numeric(names(influential))
y=y[-influential]
xaux=x[-influential]
Model01<-glm((y)~xaux,family=Gamma(link=log))
summary(Model01)
## 
## Call:
## glm(formula = (y) ~ xaux, family = Gamma(link = log))
## 
## Deviance Residuals: 
##      Min        1Q    Median        3Q       Max  
## -0.39509  -0.07968   0.03417   0.09419   0.29282  
## 
## Coefficients:
##                      Estimate Std. Error t value Pr(>|t|)    
## (Intercept)           2.95876    0.01822 162.382   <2e-16 ***
## xauxstate of alarm 2  0.04732    0.02450   1.932   0.0553 .  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for Gamma family taken to be 0.02224438)
## 
##     Null deviance: 3.6419  on 149  degrees of freedom
## Residual deviance: 3.5591  on 148  degrees of freedom
## AIC: 763.03
## 
## Number of Fisher Scoring iterations: 4
Model01<-glm((z)~x,family=Gamma(link=log))
cooksd <- cooks.distance(Model01)
influential <- cooksd[(cooksd > (3 * mean(cooksd, na.rm = TRUE)))]
influential=as.numeric(names(influential))
z=z[-influential]
xaux=x[-influential]

Model01<-glm((z)~xaux,family=Gamma(link=log))
summary(Model01)
## 
## Call:
## glm(formula = (z) ~ xaux, family = Gamma(link = log))
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -1.7334  -1.0130  -0.2200   0.4448   1.3839  
## 
## Coefficients:
##                      Estimate Std. Error t value Pr(>|t|)    
## (Intercept)            1.7492     0.1006  17.388   <2e-16 ***
## xauxstate of alarm 2   0.3069     0.1345   2.281   0.0239 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for Gamma family taken to be 0.6780472)
## 
##     Null deviance: 130.96  on 151  degrees of freedom
## Residual deviance: 127.48  on 150  degrees of freedom
## AIC: 887.88
## 
## Number of Fisher Scoring iterations: 5
data02=subset(data1,data1$Covid=="Reference after state of alarm" | data1$Covid=="after state of alarm")
boxplot(data02$euros~data02$Covid, col = rgb(0, 0.5, 1, alpha = 0.5),xlab="Covid Category",ylab="Price")

boxplot(data02$kgs~data02$Covid, col = rgb(0, 0.5, 1, alpha = 0.5),xlab="Covid Category",ylab="Abundance")

x=as.factor(data02$Covid)
table(x)
## x
##           after state of alarm Reference after state of alarm 
##                             67                             74
y=as.numeric(data02$euros)
x=relevel(x, ref = "Reference after state of alarm")
z=as.numeric(data02$kgs)

Model02<-glm((y)~x,family=Gamma(link=log))
cooksd <- cooks.distance(Model02)
influential <- cooksd[(cooksd > (3 * mean(cooksd, na.rm = TRUE)))]
influential=as.numeric(names(influential))
y=y[-influential]
xaux=x[-influential]
Model02<-glm((y)~xaux,family=Gamma(link=log))
summary(Model02)
## 
## Call:
## glm(formula = (y) ~ xaux, family = Gamma(link = log))
## 
## Deviance Residuals: 
##      Min        1Q    Median        3Q       Max  
## -0.47763  -0.09529   0.02406   0.09649   0.21779  
## 
## Coefficients:
##                          Estimate Std. Error t value Pr(>|t|)    
## (Intercept)               3.09376    0.01678  184.37  < 2e-16 ***
## xauxafter state of alarm -0.07879    0.02470   -3.19  0.00179 ** 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for Gamma family taken to be 0.01971062)
## 
##     Null deviance: 3.0060  on 129  degrees of freedom
## Residual deviance: 2.8059  on 128  degrees of freedom
## AIC: 668.85
## 
## Number of Fisher Scoring iterations: 4
Model02<-glm((z)~x,family=Gamma(link=log))
cooksd <- cooks.distance(Model02)
influential <- cooksd[(cooksd > (3 * mean(cooksd, na.rm = TRUE)))]
influential=as.numeric(names(influential))
z=z[-influential]
xaux=x[-influential]

Model02<-glm((z)~xaux,family=Gamma(link=log))
summary(Model02)
## 
## Call:
## glm(formula = (z) ~ xaux, family = Gamma(link = log))
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -2.1963  -0.9704  -0.1454   0.4405   1.5065  
## 
## Coefficients:
##                          Estimate Std. Error t value Pr(>|t|)    
## (Intercept)               2.46146    0.09884  24.903  < 2e-16 ***
## xauxafter state of alarm -0.68456    0.14429  -4.744  5.5e-06 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for Gamma family taken to be 0.6740892)
## 
##     Null deviance: 136.10  on 129  degrees of freedom
## Residual deviance: 121.42  on 128  degrees of freedom
## AIC: 820.84
## 
## Number of Fisher Scoring iterations: 5

2.11 Xiphias gladius

data1=subset(data_A,data_A$specie=="SWO-PEZ ESPADA O EMPERADOR" )

Is gamma a correct distribution for the response variable PRICE?

summary(data1$euros)
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##   2.893   5.642   6.411   6.582   7.698  11.400
a=fitdist(data1$euros, distr = "gamma",start=list(shape = 1, rate = 2), lower = -1,method = "mme")
plot(a)

ks.test(na.omit(data1$euros), "pgamma", a$estimate[1],a$estimate[2])
## 
##  One-sample Kolmogorov-Smirnov test
## 
## data:  na.omit(data1$euros)
## D = 0.096704, p-value = 0.2117
## alternative hypothesis: two-sided
gamma_test(data1$euros)
## 
##  Test of fit for the Gamma distribution
## 
## data:  data1$euros
## V = -2.4143, p-value = 0.08779

Is gamma a correct distribution for the response variable ABUNDANCE?

summary(data1$kgs)
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##      22    1012    2792    4060    5738   31324
a=fitdist(data1$kgs, distr = "gamma",start=list(shape = 1, rate = 2), lower = -1,method = "mme")
plot(a)

ks.test(na.omit(data1$kgs), "pgamma", a$estimate[1],a$estimate[2])
## 
##  One-sample Kolmogorov-Smirnov test
## 
## data:  na.omit(data1$kgs)
## D = 0.074699, p-value = 0.5147
## alternative hypothesis: two-sided
gamma_test(data1$kgs)
## 
##  Test of fit for the Gamma distribution
## 
## data:  data1$kgs
## V = -0.73367, p-value = 0.6039

Regression models

x=as.factor(data1$Covid)
levels(x)
## [1] "after state of alarm"           "Reference after state of alarm"
## [3] "Reference No"                   "Reference state of alarm 1"    
## [5] "Reference state of alarm 2"     "state of alarm 1"              
## [7] "state of alarm 2"
data0=subset(data1,data1$Covid=="Reference state of alarm 1" | data1$Covid=="state of alarm 1")
boxplot(data0$euros~data0$Covid, col = rgb(0, 0.5, 1, alpha = 0.5),xlab="Covid Category",ylab="Price")

boxplot(data0$kgs~data0$Covid, col = rgb(0, 0.5, 1, alpha = 0.5),xlab="Covid Category",ylab="Abundance")

x=as.factor(data0$Covid)
table(x)
## x
## Reference state of alarm 1           state of alarm 1 
##                         10                         18
y=as.numeric(data0$euros)
x=relevel(x, ref = "Reference state of alarm 1")
z=as.numeric(data0$kgs)

Model0<-glm((y)~x,family=Gamma(link=log))
cooksd <- cooks.distance(Model0)
influential <- cooksd[(cooksd > (3 * mean(cooksd, na.rm = TRUE)))]
influential=as.numeric(names(influential))
y=y[-influential]
xaux=x[-influential]
Model0<-glm((y)~xaux,family=Gamma(link=log))
summary(Model0)
## 
## Call:
## glm(formula = (y) ~ xaux, family = Gamma(link = log))
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -0.4964  -0.1401   0.0154   0.2154   0.2154  
## 
## Coefficients:
##                      Estimate Std. Error t value Pr(>|t|)    
## (Intercept)           2.06615    0.06705  30.815   <2e-16 ***
## xauxstate of alarm 1 -0.19463    0.08450  -2.303   0.0299 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for Gamma family taken to be 0.04495864)
## 
##     Null deviance: 1.4874  on 26  degrees of freedom
## Residual deviance: 1.2452  on 25  degrees of freedom
## AIC: 103.47
## 
## Number of Fisher Scoring iterations: 4
Model0<-glm((z)~x,family=Gamma(link=log))
cooksd <- cooks.distance(Model0)
influential <- cooksd[(cooksd > (3 * mean(cooksd, na.rm = TRUE)))]
influential=as.numeric(names(influential))
z=z[-influential]
xaux=x[-influential]

Model0<-glm((z)~xaux,family=Gamma(link=log))
summary(Model0)
## 
## Call:
## glm(formula = (z) ~ xaux, family = Gamma(link = log))
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -2.3571  -1.9396  -0.6478   0.4096   1.6352  
## 
## Coefficients:
##                      Estimate Std. Error t value Pr(>|t|)    
## (Intercept)            7.5248     0.3900  19.295 4.04e-16 ***
## xauxstate of alarm 1  -0.5121     0.4823  -1.062    0.299    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for Gamma family taken to be 1.368741)
## 
##     Null deviance: 57.727  on 25  degrees of freedom
## Residual deviance: 56.116  on 24  degrees of freedom
## AIC: 426.08
## 
## Number of Fisher Scoring iterations: 8
data01=subset(data1,data1$Covid=="Reference state of alarm 2" | data1$Covid=="state of alarm 2")
boxplot(data01$euros~data01$Covid, col = rgb(0, 0.5, 1, alpha = 0.5),xlab="Covid Category",ylab="Price")

boxplot(data01$kgs~data01$Covid, col = rgb(0, 0.5, 1, alpha = 0.5),xlab="Covid Category",ylab="Abundance")

x=as.factor(data01$Covid)
table(x)
## x
## Reference state of alarm 2           state of alarm 2 
##                         23                         34
y=as.numeric(data01$euros)
x=relevel(x, ref = "Reference state of alarm 2")
z=as.numeric(data01$kgs)


Model01<-glm((y)~x,family=Gamma(link=log))
cooksd <- cooks.distance(Model01)
influential <- cooksd[(cooksd > (3 * mean(cooksd, na.rm = TRUE)))]
influential=as.numeric(names(influential))
y=y[-influential]
xaux=x[-influential]
Model01<-glm((y)~xaux,family=Gamma(link=log))
summary(Model01)
## 
## Call:
## glm(formula = (y) ~ xaux, family = Gamma(link = log))
## 
## Deviance Residuals: 
##      Min        1Q    Median        3Q       Max  
## -0.30183  -0.13773   0.01005   0.10668   0.30157  
## 
## Coefficients:
##                      Estimate Std. Error t value Pr(>|t|)    
## (Intercept)           1.92725    0.03209  60.054   <2e-16 ***
## xauxstate of alarm 2 -0.07762    0.04256  -1.824   0.0743 .  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for Gamma family taken to be 0.02265726)
## 
##     Null deviance: 1.1840  on 50  degrees of freedom
## Residual deviance: 1.1084  on 49  degrees of freedom
## AIC: 146.61
## 
## Number of Fisher Scoring iterations: 4
Model01<-glm((z)~x,family=Gamma(link=log))
cooksd <- cooks.distance(Model01)
influential <- cooksd[(cooksd > (3 * mean(cooksd, na.rm = TRUE)))]
influential=as.numeric(names(influential))
z=z[-influential]
xaux=x[-influential]

Model01<-glm((z)~xaux,family=Gamma(link=log))
summary(Model01)
## 
## Call:
## glm(formula = (z) ~ xaux, family = Gamma(link = log))
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -2.7738  -1.0328  -0.1919   0.4749   1.4288  
## 
## Coefficients:
##                      Estimate Std. Error t value Pr(>|t|)    
## (Intercept)            8.6431     0.1786  48.397  < 2e-16 ***
## xauxstate of alarm 2  -0.7129     0.2357  -3.024  0.00387 ** 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for Gamma family taken to be 0.733548)
## 
##     Null deviance: 76.690  on 53  degrees of freedom
## Residual deviance: 69.884  on 52  degrees of freedom
## AIC: 1003.7
## 
## Number of Fisher Scoring iterations: 6
data02=subset(data1,data1$Covid=="Reference after state of alarm" | data1$Covid=="after state of alarm")
boxplot(data02$euros~data02$Covid, col = rgb(0, 0.5, 1, alpha = 0.5),xlab="Covid Category",ylab="Price")

boxplot(data02$kgs~data02$Covid, col = rgb(0, 0.5, 1, alpha = 0.5),xlab="Covid Category",ylab="Abundance")

x=as.factor(data02$Covid)
table(x)
## x
##           after state of alarm Reference after state of alarm 
##                             14                             14
y=as.numeric(data02$euros)
x=relevel(x, ref = "Reference after state of alarm")
z=as.numeric(data02$kgs)

Model02<-glm((y)~x,family=Gamma(link=log))
cooksd <- cooks.distance(Model02)
influential <- cooksd[(cooksd > (3 * mean(cooksd, na.rm = TRUE)))]
influential=as.numeric(names(influential))
y=y[-influential]
xaux=x[-influential]
Model02<-glm((y)~xaux,family=Gamma(link=log))
summary(Model02)
## 
## Call:
## glm(formula = (y) ~ xaux, family = Gamma(link = log))
## 
## Deviance Residuals: 
##      Min        1Q    Median        3Q       Max  
## -0.22823  -0.04157  -0.01348   0.05291   0.18024  
## 
## Coefficients:
##                          Estimate Std. Error t value Pr(>|t|)    
## (Intercept)               1.77232    0.02887  61.394   <2e-16 ***
## xauxafter state of alarm -0.01578    0.04083  -0.387    0.702    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for Gamma family taken to be 0.0108338)
## 
##     Null deviance: 0.26692  on 25  degrees of freedom
## Residual deviance: 0.26530  on 24  degrees of freedom
## AIC: 52.105
## 
## Number of Fisher Scoring iterations: 4
Model02<-glm((z)~x,family=Gamma(link=log))
cooksd <- cooks.distance(Model02)
influential <- cooksd[(cooksd > (3 * mean(cooksd, na.rm = TRUE)))]
influential=as.numeric(names(influential))
z=z[-influential]
xaux=x[-influential]

Model02<-glm((z)~xaux,family=Gamma(link=log))
summary(Model02)
## 
## Call:
## glm(formula = (z) ~ xaux, family = Gamma(link = log))
## 
## Deviance Residuals: 
##      Min        1Q    Median        3Q       Max  
## -1.17530  -0.20725  -0.03169   0.20002   0.72656  
## 
## Coefficients:
##                          Estimate Std. Error t value Pr(>|t|)    
## (Intercept)                8.4755     0.1250  67.814   <2e-16 ***
## xauxafter state of alarm  -0.3882     0.1767  -2.196   0.0389 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for Gamma family taken to be 0.1874422)
## 
##     Null deviance: 6.5388  on 23  degrees of freedom
## Residual deviance: 5.6403  on 22  degrees of freedom
## AIC: 432.16
## 
## Number of Fisher Scoring iterations: 4

2.12 Caranx rhonchus

data1=subset(data_A,data_A$specie=="HMY-JURELA O JUREL DORADO" )

Is gamma a correct distribution for the response variable PRICE?

summary(data1$euros)
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##  0.1024  2.6221  3.3410  3.3917  4.0331 15.5000
a=fitdist(data1$euros, distr = "gamma",start=list(shape = 1, rate = 2), lower = -1,method = "mme")
plot(a)

ks.test(na.omit(data1$euros), "pgamma", a$estimate[1],a$estimate[2])
## 
##  One-sample Kolmogorov-Smirnov test
## 
## data:  na.omit(data1$euros)
## D = 0.08412, p-value = 0.001643
## alternative hypothesis: two-sided
gamma_test(data1$euros)
## 
##  Test of fit for the Gamma distribution
## 
## data:  data1$euros
## V = -0.66654, p-value = 0.6374

Is gamma a correct distribution for the response variable ABUNDANCE?

summary(data1$kgs)
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##    0.10   10.29   35.58   84.20   91.48 5702.51
a=fitdist(data1$kgs, distr = "gamma",start=list(shape = 1, rate = 2), lower = -1,method = "mme")
plot(a)

ks.test(na.omit(data1$kgs), "pgamma", a$estimate[1],a$estimate[2])
## 
##  One-sample Kolmogorov-Smirnov test
## 
## data:  na.omit(data1$kgs)
## D = 0.52547, p-value < 2.2e-16
## alternative hypothesis: two-sided
gamma_test(data1$kgs)
## 
##  Test of fit for the Gamma distribution
## 
## data:  data1$kgs
## V = 59.199, p-value < 2.2e-16

Regression models

x=as.factor(data1$Covid)
levels(x)
## [1] "after state of alarm"           "Reference after state of alarm"
## [3] "Reference No"                   "Reference state of alarm 1"    
## [5] "Reference state of alarm 2"     "state of alarm 1"              
## [7] "state of alarm 2"
data0=subset(data1,data1$Covid=="Reference state of alarm 1" | data1$Covid=="state of alarm 1")
boxplot(data0$euros~data0$Covid, col = rgb(0, 0.5, 1, alpha = 0.5),xlab="Covid Category",ylab="Price")

boxplot(data0$kgs~data0$Covid, col = rgb(0, 0.5, 1, alpha = 0.5),xlab="Covid Category",ylab="Abundance")

x=as.factor(data0$Covid)
table(x)
## x
## Reference state of alarm 1           state of alarm 1 
##                         66                         69
y=as.numeric(data0$euros)
x=relevel(x, ref = "Reference state of alarm 1")
z=as.numeric(data0$kgs)

Model0<-glm((y)~x,family=Gamma(link=log))
cooksd <- cooks.distance(Model0)
influential <- cooksd[(cooksd > (3 * mean(cooksd, na.rm = TRUE)))]
influential=as.numeric(names(influential))
y=y[-influential]
xaux=x[-influential]
Model0<-glm((y)~xaux,family=Gamma(link=log))
summary(Model0)
## 
## Call:
## glm(formula = (y) ~ xaux, family = Gamma(link = log))
## 
## Deviance Residuals: 
##      Min        1Q    Median        3Q       Max  
## -0.53983  -0.13686  -0.01217   0.14379   0.40983  
## 
## Coefficients:
##                      Estimate Std. Error t value Pr(>|t|)    
## (Intercept)           1.17178    0.02556  45.844  < 2e-16 ***
## xauxstate of alarm 1  0.12135    0.03461   3.506 0.000642 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for Gamma family taken to be 0.03593246)
## 
##     Null deviance: 4.9413  on 120  degrees of freedom
## Residual deviance: 4.5015  on 119  degrees of freedom
## AIC: 246.96
## 
## Number of Fisher Scoring iterations: 4
Model0<-glm((z)~x,family=Gamma(link=log))
cooksd <- cooks.distance(Model0)
influential <- cooksd[(cooksd > (3 * mean(cooksd, na.rm = TRUE)))]
influential=as.numeric(names(influential))
z=z[-influential]
xaux=x[-influential]

Model0<-glm((z)~xaux,family=Gamma(link=log))
summary(Model0)
## 
## Call:
## glm(formula = (z) ~ xaux, family = Gamma(link = log))
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -2.7928  -1.0893  -0.1753   0.4516   1.6979  
## 
## Coefficients:
##                      Estimate Std. Error t value Pr(>|t|)    
## (Intercept)            4.4768     0.1112  40.277   <2e-16 ***
## xauxstate of alarm 1  -0.2291     0.1554  -1.474    0.143    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for Gamma family taken to be 0.7783275)
## 
##     Null deviance: 172.76  on 128  degrees of freedom
## Residual deviance: 171.07  on 127  degrees of freedom
## AIC: 1389.5
## 
## Number of Fisher Scoring iterations: 6
data01=subset(data1,data1$Covid=="Reference state of alarm 2" | data1$Covid=="state of alarm 2")
boxplot(data01$euros~data01$Covid, col = rgb(0, 0.5, 1, alpha = 0.5),xlab="Covid Category",ylab="Price")

boxplot(data01$kgs~data01$Covid, col = rgb(0, 0.5, 1, alpha = 0.5),xlab="Covid Category",ylab="Abundance")

x=as.factor(data01$Covid)
table(x)
## x
## Reference state of alarm 2           state of alarm 2 
##                         65                        103
y=as.numeric(data01$euros)
x=relevel(x, ref = "Reference state of alarm 2")
z=as.numeric(data01$kgs)


Model01<-glm((y)~x,family=Gamma(link=log))
cooksd <- cooks.distance(Model01)
influential <- cooksd[(cooksd > (3 * mean(cooksd, na.rm = TRUE)))]
influential=as.numeric(names(influential))
y=y[-influential]
xaux=x[-influential]
Model01<-glm((y)~xaux,family=Gamma(link=log))
summary(Model01)
## 
## Call:
## glm(formula = (y) ~ xaux, family = Gamma(link = log))
## 
## Deviance Residuals: 
##      Min        1Q    Median        3Q       Max  
## -1.57977  -0.31260  -0.00686   0.24254   0.87433  
## 
## Coefficients:
##                      Estimate Std. Error t value Pr(>|t|)    
## (Intercept)           1.10589    0.05240  21.103   <2e-16 ***
## xauxstate of alarm 2  0.01820    0.06688   0.272    0.786    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for Gamma family taken to be 0.1675195)
## 
##     Null deviance: 33.204  on 157  degrees of freedom
## Residual deviance: 33.192  on 156  degrees of freedom
## AIC: 533.18
## 
## Number of Fisher Scoring iterations: 4
Model01<-glm((z)~x,family=Gamma(link=log))
cooksd <- cooks.distance(Model01)
influential <- cooksd[(cooksd > (3 * mean(cooksd, na.rm = TRUE)))]
influential=as.numeric(names(influential))
z=z[-influential]
xaux=x[-influential]

Model01<-glm((z)~xaux,family=Gamma(link=log))
summary(Model01)
## 
## Call:
## glm(formula = (z) ~ xaux, family = Gamma(link = log))
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -3.1584  -1.4052  -0.6743   0.2321   4.3443  
## 
## Coefficients:
##                      Estimate Std. Error t value Pr(>|t|)    
## (Intercept)            3.3391     0.2000   16.70   <2e-16 ***
## xauxstate of alarm 2   0.3435     0.2544    1.35    0.179    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for Gamma family taken to be 2.519852)
## 
##     Null deviance: 305.63  on 164  degrees of freedom
## Residual deviance: 301.18  on 163  degrees of freedom
## AIC: 1491
## 
## Number of Fisher Scoring iterations: 6
data02=subset(data1,data1$Covid=="Reference after state of alarm" | data1$Covid=="after state of alarm")
boxplot(data02$euros~data02$Covid, col = rgb(0, 0.5, 1, alpha = 0.5),xlab="Covid Category",ylab="Price")

boxplot(data02$kgs~data02$Covid, col = rgb(0, 0.5, 1, alpha = 0.5),xlab="Covid Category",ylab="Abundance")

x=as.factor(data02$Covid)
table(x)
## x
##           after state of alarm Reference after state of alarm 
##                             92                             88
y=as.numeric(data02$euros)
x=relevel(x, ref = "Reference after state of alarm")
z=as.numeric(data02$kgs)

Model02<-glm((y)~x,family=Gamma(link=log))
cooksd <- cooks.distance(Model02)
influential <- cooksd[(cooksd > (3 * mean(cooksd, na.rm = TRUE)))]
influential=as.numeric(names(influential))
y=y[-influential]
xaux=x[-influential]
Model02<-glm((y)~xaux,family=Gamma(link=log))
summary(Model02)
## 
## Call:
## glm(formula = (y) ~ xaux, family = Gamma(link = log))
## 
## Deviance Residuals: 
##      Min        1Q    Median        3Q       Max  
## -0.47851  -0.13881   0.00736   0.12772   0.42958  
## 
## Coefficients:
##                          Estimate Std. Error t value Pr(>|t|)    
## (Intercept)               1.07387    0.02258  47.565  < 2e-16 ***
## xauxafter state of alarm  0.23016    0.03100   7.425 6.06e-12 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for Gamma family taken to be 0.03924782)
## 
##     Null deviance: 8.7445  on 163  degrees of freedom
## Residual deviance: 6.5954  on 162  degrees of freedom
## AIC: 331.18
## 
## Number of Fisher Scoring iterations: 4
Model02<-glm((z)~x,family=Gamma(link=log))
cooksd <- cooks.distance(Model02)
influential <- cooksd[(cooksd > (3 * mean(cooksd, na.rm = TRUE)))]
influential=as.numeric(names(influential))
z=z[-influential]
xaux=x[-influential]

Model02<-glm((z)~xaux,family=Gamma(link=log))
summary(Model02)
## 
## Call:
## glm(formula = (z) ~ xaux, family = Gamma(link = log))
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -2.9848  -0.8135  -0.2063   0.4988   1.4232  
## 
## Coefficients:
##                          Estimate Std. Error t value Pr(>|t|)    
## (Intercept)               3.71140    0.08751  42.413  < 2e-16 ***
## xauxafter state of alarm  0.87127    0.12304   7.081 3.61e-11 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for Gamma family taken to be 0.6508826)
## 
##     Null deviance: 214.98  on 171  degrees of freedom
## Residual deviance: 183.42  on 170  degrees of freedom
## AIC: 1779.7
## 
## Number of Fisher Scoring iterations: 6

2.13 Boops boops

data1=subset(data_A,data_A$specie=="BOG-BOGA" )

Is gamma a correct distribution for the response variable PRICE?

summary(data1$euros)
##      Min.   1st Qu.    Median      Mean   3rd Qu.      Max. 
##  0.009987  0.075151  0.260007  0.455654  0.400949 27.650000
a=fitdist(data1$euros, distr = "gamma",start=list(shape = 1, rate = 2), lower = -1,method = "mme")
plot(a)

ks.test(na.omit(data1$euros), "pgamma", a$estimate[1],a$estimate[2])
## 
##  One-sample Kolmogorov-Smirnov test
## 
## data:  na.omit(data1$euros)
## D = 0.7789, p-value < 2.2e-16
## alternative hypothesis: two-sided
gamma_test(data1$euros)
## 
##  Test of fit for the Gamma distribution
## 
## data:  data1$euros
## V = 78.691, p-value < 2.2e-16

Is gamma a correct distribution for the response variable ABUNDANCE?

summary(data1$kgs)
##     Min.  1st Qu.   Median     Mean  3rd Qu.     Max. 
##     2.81    30.65   202.98  1228.49  1520.09 10600.00
a=fitdist(data1$kgs, distr = "gamma",start=list(shape = 1, rate = 2), lower = -1,method = "mme")
plot(a)

ks.test(na.omit(data1$kgs), "pgamma", a$estimate[1],a$estimate[2])
## 
##  One-sample Kolmogorov-Smirnov test
## 
## data:  na.omit(data1$kgs)
## D = 0.1203, p-value = 0.01158
## alternative hypothesis: two-sided
gamma_test(data1$kgs)
## 
##  Test of fit for the Gamma distribution
## 
## data:  data1$kgs
## V = 0.53884, p-value = 0.7032

Regression models

x=as.factor(data1$Covid)
levels(x)
## [1] "after state of alarm"           "Reference after state of alarm"
## [3] "Reference No"                   "Reference state of alarm 1"    
## [5] "Reference state of alarm 2"     "state of alarm 1"              
## [7] "state of alarm 2"
data0=subset(data1,data1$Covid=="Reference state of alarm 1" | data1$Covid=="state of alarm 1")
boxplot(data0$euros~data0$Covid, col = rgb(0, 0.5, 1, alpha = 0.5),xlab="Covid Category",ylab="Price")

boxplot(data0$kgs~data0$Covid, col = rgb(0, 0.5, 1, alpha = 0.5),xlab="Covid Category",ylab="Abundance")

x=as.factor(data0$Covid)
table(x)
## x
## Reference state of alarm 1           state of alarm 1 
##                         34                         28
y=as.numeric(data0$euros)
x=relevel(x, ref = "Reference state of alarm 1")
z=as.numeric(data0$kgs)

Model0<-glm((y)~x,family=Gamma(link=log))
cooksd <- cooks.distance(Model0)
influential <- cooksd[(cooksd > (3 * mean(cooksd, na.rm = TRUE)))]
influential=as.numeric(names(influential))
y=y[-influential]
xaux=x[-influential]
Model0<-glm((y)~xaux,family=Gamma(link=log))
summary(Model0)
## 
## Call:
## glm(formula = (y) ~ xaux, family = Gamma(link = log))
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -1.6873  -0.7345  -0.1761   0.4731   1.6196  
## 
## Coefficients:
##                      Estimate Std. Error t value Pr(>|t|)    
## (Intercept)           -1.3807     0.1285  -10.75 1.62e-15 ***
## xauxstate of alarm 1  -0.4420     0.1896   -2.33   0.0232 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for Gamma family taken to be 0.5447963)
## 
##     Null deviance: 35.754  on 60  degrees of freedom
## Residual deviance: 32.853  on 59  degrees of freedom
## AIC: -79.265
## 
## Number of Fisher Scoring iterations: 5
Model0<-glm((z)~x,family=Gamma(link=log))
cooksd <- cooks.distance(Model0)
influential <- cooksd[(cooksd > (3 * mean(cooksd, na.rm = TRUE)))]
influential=as.numeric(names(influential))
z=z[-influential]
xaux=x[-influential]

Model0<-glm((z)~xaux,family=Gamma(link=log))
summary(Model0)
## 
## Call:
## glm(formula = (z) ~ xaux, family = Gamma(link = log))
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -3.1307  -2.1336  -1.4009  -0.0763   3.6650  
## 
## Coefficients:
##                      Estimate Std. Error t value Pr(>|t|)    
## (Intercept)            6.2125     0.3529  17.605   <2e-16 ***
## xauxstate of alarm 1   1.2746     0.5133   2.483   0.0162 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for Gamma family taken to be 3.611438)
## 
##     Null deviance: 209.36  on 54  degrees of freedom
## Residual deviance: 188.00  on 53  degrees of freedom
## AIC: 817.84
## 
## Number of Fisher Scoring iterations: 10
data01=subset(data1,data1$Covid=="Reference state of alarm 2" | data1$Covid=="state of alarm 2")
boxplot(data01$euros~data01$Covid, col = rgb(0, 0.5, 1, alpha = 0.5),xlab="Covid Category",ylab="Price")

boxplot(data01$kgs~data01$Covid, col = rgb(0, 0.5, 1, alpha = 0.5),xlab="Covid Category",ylab="Abundance")

x=as.factor(data01$Covid)
table(x)
## x
## Reference state of alarm 2           state of alarm 2 
##                         24                         42
y=as.numeric(data01$euros)
x=relevel(x, ref = "Reference state of alarm 2")
z=as.numeric(data01$kgs)


Model01<-glm((y)~x,family=Gamma(link=log))
cooksd <- cooks.distance(Model01)
influential <- cooksd[(cooksd > (3 * mean(cooksd, na.rm = TRUE)))]
influential=as.numeric(names(influential))
y=y[-influential]
xaux=x[-influential]
Model01<-glm((y)~xaux,family=Gamma(link=log))
summary(Model01)
## 
## Call:
## glm(formula = (y) ~ xaux, family = Gamma(link = log))
## 
## Deviance Residuals: 
##      Min        1Q    Median        3Q       Max  
## -1.21824  -0.92223   0.03151   0.38808   0.87711  
## 
## Coefficients:
##                      Estimate Std. Error t value Pr(>|t|)    
## (Intercept)           -1.2808     0.1342  -9.541 1.45e-13 ***
## xauxstate of alarm 2   0.1440     0.1637   0.880    0.383    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for Gamma family taken to be 0.3604249)
## 
##     Null deviance: 29.960  on 60  degrees of freedom
## Residual deviance: 29.685  on 59  degrees of freedom
## AIC: -34.045
## 
## Number of Fisher Scoring iterations: 5
Model01<-glm((z)~x,family=Gamma(link=log))
cooksd <- cooks.distance(Model01)
influential <- cooksd[(cooksd > (3 * mean(cooksd, na.rm = TRUE)))]
influential=as.numeric(names(influential))
z=z[-influential]
xaux=x[-influential]

Model01<-glm((z)~xaux,family=Gamma(link=log))
summary(Model01)
## 
## Call:
## glm(formula = (z) ~ xaux, family = Gamma(link = log))
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -2.6401  -1.7368  -0.8162   0.1626   2.5795  
## 
## Coefficients:
##                      Estimate Std. Error t value Pr(>|t|)    
## (Intercept)           6.62258    0.29396   22.53   <2e-16 ***
## xauxstate of alarm 2 -0.06587    0.36598   -0.18    0.858    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for Gamma family taken to be 1.901133)
## 
##     Null deviance: 135.81  on 61  degrees of freedom
## Residual deviance: 135.75  on 60  degrees of freedom
## AIC: 931.31
## 
## Number of Fisher Scoring iterations: 9
data02=subset(data1,data1$Covid=="Reference after state of alarm" | data1$Covid=="after state of alarm")
boxplot(data02$euros~data02$Covid, col = rgb(0, 0.5, 1, alpha = 0.5),xlab="Covid Category",ylab="Price")

boxplot(data02$kgs~data02$Covid, col = rgb(0, 0.5, 1, alpha = 0.5),xlab="Covid Category",ylab="Abundance")

x=as.factor(data02$Covid)
table(x)
## x
##           after state of alarm Reference after state of alarm 
##                             17                             18
y=as.numeric(data02$euros)
x=relevel(x, ref = "Reference after state of alarm")
z=as.numeric(data02$kgs)

Model02<-glm((y)~x,family=Gamma(link=log))
cooksd <- cooks.distance(Model02)
influential <- cooksd[(cooksd > (3 * mean(cooksd, na.rm = TRUE)))]
influential=as.numeric(names(influential))
y=y[-influential]
xaux=x[-influential]
Model02<-glm((y)~xaux,family=Gamma(link=log))
summary(Model02)
## 
## Call:
## glm(formula = (y) ~ xaux, family = Gamma(link = log))
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -2.1852  -0.4472  -0.3848   0.0995   1.8082  
## 
## Coefficients:
##                          Estimate Std. Error t value Pr(>|t|)    
## (Intercept)               -1.2540     0.2106  -5.955 1.58e-06 ***
## xauxafter state of alarm  -0.9220     0.3076  -2.998  0.00542 ** 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for Gamma family taken to be 0.7537919)
## 
##     Null deviance: 24.03  on 31  degrees of freedom
## Residual deviance: 17.60  on 30  degrees of freedom
## AIC: -44.931
## 
## Number of Fisher Scoring iterations: 5
Model02<-glm((z)~x,family=Gamma(link=log))
cooksd <- cooks.distance(Model02)
influential <- cooksd[(cooksd > (3 * mean(cooksd, na.rm = TRUE)))]
influential=as.numeric(names(influential))
z=z[-influential]
xaux=x[-influential]

Model02<-glm((z)~xaux,family=Gamma(link=log))
summary(Model02)
## 
## Call:
## glm(formula = (z) ~ xaux, family = Gamma(link = log))
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -2.8570  -2.2203  -1.2086   0.8709   2.0940  
## 
## Coefficients:
##                          Estimate Std. Error t value Pr(>|t|)    
## (Intercept)                4.3499     0.3106  14.006 1.07e-14 ***
## xauxafter state of alarm   2.4116     0.4536   5.316 9.54e-06 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for Gamma family taken to be 1.639837)
## 
##     Null deviance: 133.765  on 31  degrees of freedom
## Residual deviance:  94.068  on 30  degrees of freedom
## AIC: 401.63
## 
## Number of Fisher Scoring iterations: 12

2.14 Aristeus antennatus

data1=subset(data_A,data_A$specie=="ARA-GAMBA ROJA O RAYADO" )

Is gamma a correct distribution for the response variable PRICE?

summary(data1$euros)
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##   10.70   29.43   34.36   36.46   42.23   93.58
a=fitdist(data1$euros, distr = "gamma",start=list(shape = 1, rate = 2), lower = -1,method = "mme")
plot(a)

ks.test(na.omit(data1$euros), "pgamma", a$estimate[1],a$estimate[2])
## 
##  One-sample Kolmogorov-Smirnov test
## 
## data:  na.omit(data1$euros)
## D = 0.055168, p-value = 0.1137
## alternative hypothesis: two-sided
gamma_test(data1$euros)
## 
##  Test of fit for the Gamma distribution
## 
## data:  data1$euros
## V = 5.4031, p-value = 0.0001331

Is gamma a correct distribution for the response variable ABUNDANCE?

summary(data1$kgs)
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##    0.30   33.23  135.70  330.40  409.34 3308.44
a=fitdist(data1$kgs, distr = "gamma",start=list(shape = 1, rate = 2), lower = -1,method = "mme")
plot(a)

ks.test(na.omit(data1$kgs), "pgamma", a$estimate[1],a$estimate[2])
## 
##  One-sample Kolmogorov-Smirnov test
## 
## data:  na.omit(data1$kgs)
## D = 0.04059, p-value = 0.4196
## alternative hypothesis: two-sided
gamma_test(data1$kgs)
## 
##  Test of fit for the Gamma distribution
## 
## data:  data1$kgs
## V = 0.73613, p-value = 0.6027

Regression models

x=as.factor(data1$Covid)
levels(x)
## [1] "after state of alarm"           "Reference after state of alarm"
## [3] "Reference No"                   "Reference state of alarm 1"    
## [5] "Reference state of alarm 2"     "state of alarm 1"              
## [7] "state of alarm 2"
data0=subset(data1,data1$Covid=="Reference state of alarm 1" | data1$Covid=="state of alarm 1")
boxplot(data0$euros~data0$Covid, col = rgb(0, 0.5, 1, alpha = 0.5),xlab="Covid Category",ylab="Price")

boxplot(data0$kgs~data0$Covid, col = rgb(0, 0.5, 1, alpha = 0.5),xlab="Covid Category",ylab="Abundance")

x=as.factor(data0$Covid)
table(x)
## x
## Reference state of alarm 1           state of alarm 1 
##                         65                         52
y=as.numeric(data0$euros)
x=relevel(x, ref = "Reference state of alarm 1")
z=as.numeric(data0$kgs)

Model0<-glm((y)~x,family=Gamma(link=log))
cooksd <- cooks.distance(Model0)
influential <- cooksd[(cooksd > (3 * mean(cooksd, na.rm = TRUE)))]
influential=as.numeric(names(influential))
y=y[-influential]
xaux=x[-influential]
Model0<-glm((y)~xaux,family=Gamma(link=log))
summary(Model0)
## 
## Call:
## glm(formula = (y) ~ xaux, family = Gamma(link = log))
## 
## Deviance Residuals: 
##       Min         1Q     Median         3Q        Max  
## -0.244508  -0.090764  -0.005719   0.082665   0.228938  
## 
## Coefficients:
##                      Estimate Std. Error t value Pr(>|t|)    
## (Intercept)           3.36221    0.01474 228.163   <2e-16 ***
## xauxstate of alarm 1 -0.01663    0.02283  -0.728    0.468    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for Gamma family taken to be 0.01368042)
## 
##     Null deviance: 1.4641  on 107  degrees of freedom
## Residual deviance: 1.4568  on 106  degrees of freedom
## AIC: 570.98
## 
## Number of Fisher Scoring iterations: 4
Model0<-glm((z)~x,family=Gamma(link=log))
cooksd <- cooks.distance(Model0)
influential <- cooksd[(cooksd > (3 * mean(cooksd, na.rm = TRUE)))]
influential=as.numeric(names(influential))
z=z[-influential]
xaux=x[-influential]

Model0<-glm((z)~xaux,family=Gamma(link=log))
summary(Model0)
## 
## Call:
## glm(formula = (z) ~ xaux, family = Gamma(link = log))
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -3.2023  -1.0621  -0.3503   0.4594   1.5822  
## 
## Coefficients:
##                      Estimate Std. Error t value Pr(>|t|)    
## (Intercept)            5.8961     0.1169  50.448   <2e-16 ***
## xauxstate of alarm 1   0.4196     0.1751   2.396   0.0183 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for Gamma family taken to be 0.833228)
## 
##     Null deviance: 176.38  on 109  degrees of freedom
## Residual deviance: 171.55  on 108  degrees of freedom
## AIC: 1560.8
## 
## Number of Fisher Scoring iterations: 6
data01=subset(data1,data1$Covid=="Reference state of alarm 2" | data1$Covid=="state of alarm 2")
boxplot(data01$euros~data01$Covid, col = rgb(0, 0.5, 1, alpha = 0.5),xlab="Covid Category",ylab="Price")

boxplot(data01$kgs~data01$Covid, col = rgb(0, 0.5, 1, alpha = 0.5),xlab="Covid Category",ylab="Abundance")

x=as.factor(data01$Covid)
table(x)
## x
## Reference state of alarm 2           state of alarm 2 
##                         71                         94
y=as.numeric(data01$euros)
x=relevel(x, ref = "Reference state of alarm 2")
z=as.numeric(data01$kgs)


Model01<-glm((y)~x,family=Gamma(link=log))
cooksd <- cooks.distance(Model01)
influential <- cooksd[(cooksd > (3 * mean(cooksd, na.rm = TRUE)))]
influential=as.numeric(names(influential))
y=y[-influential]
xaux=x[-influential]
Model01<-glm((y)~xaux,family=Gamma(link=log))
summary(Model01)
## 
## Call:
## glm(formula = (y) ~ xaux, family = Gamma(link = log))
## 
## Deviance Residuals: 
##      Min        1Q    Median        3Q       Max  
## -0.43854  -0.11225  -0.01510   0.09624   0.36915  
## 
## Coefficients:
##                      Estimate Std. Error t value Pr(>|t|)    
## (Intercept)           3.67337    0.02119 173.372  < 2e-16 ***
## xauxstate of alarm 2  0.21575    0.02789   7.736 1.51e-12 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for Gamma family taken to be 0.0282821)
## 
##     Null deviance: 5.8778  on 148  degrees of freedom
## Residual deviance: 4.2070  on 147  degrees of freedom
## AIC: 1025.6
## 
## Number of Fisher Scoring iterations: 4
Model01<-glm((z)~x,family=Gamma(link=log))
cooksd <- cooks.distance(Model01)
influential <- cooksd[(cooksd > (3 * mean(cooksd, na.rm = TRUE)))]
influential=as.numeric(names(influential))
z=z[-influential]
xaux=x[-influential]

Model01<-glm((z)~xaux,family=Gamma(link=log))
summary(Model01)
## 
## Call:
## glm(formula = (z) ~ xaux, family = Gamma(link = log))
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -2.9953  -1.4029  -0.5891   0.3730   2.1611  
## 
## Coefficients:
##                      Estimate Std. Error t value Pr(>|t|)    
## (Intercept)            4.2777     0.1434  29.831   <2e-16 ***
## xauxstate of alarm 2   0.3490     0.1880   1.856   0.0654 .  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for Gamma family taken to be 1.316074)
## 
##     Null deviance: 260.13  on 152  degrees of freedom
## Residual deviance: 255.70  on 151  degrees of freedom
## AIC: 1674.2
## 
## Number of Fisher Scoring iterations: 6
data02=subset(data1,data1$Covid=="Reference after state of alarm" | data1$Covid=="after state of alarm")
boxplot(data02$euros~data02$Covid, col = rgb(0, 0.5, 1, alpha = 0.5),xlab="Covid Category",ylab="Price")

boxplot(data02$kgs~data02$Covid, col = rgb(0, 0.5, 1, alpha = 0.5),xlab="Covid Category",ylab="Abundance")

x=as.factor(data02$Covid)
table(x)
## x
##           after state of alarm Reference after state of alarm 
##                             84                             81
y=as.numeric(data02$euros)
x=relevel(x, ref = "Reference after state of alarm")
z=as.numeric(data02$kgs)

Model02<-glm((y)~x,family=Gamma(link=log))
cooksd <- cooks.distance(Model02)
influential <- cooksd[(cooksd > (3 * mean(cooksd, na.rm = TRUE)))]
influential=as.numeric(names(influential))
y=y[-influential]
xaux=x[-influential]
Model02<-glm((y)~xaux,family=Gamma(link=log))
summary(Model02)
## 
## Call:
## glm(formula = (y) ~ xaux, family = Gamma(link = log))
## 
## Deviance Residuals: 
##       Min         1Q     Median         3Q        Max  
## -0.293707  -0.099510  -0.007575   0.085213   0.281373  
## 
## Coefficients:
##                          Estimate Std. Error t value Pr(>|t|)    
## (Intercept)               3.42644    0.01534 223.392  < 2e-16 ***
## xauxafter state of alarm  0.15728    0.02148   7.322 1.36e-11 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for Gamma family taken to be 0.01764458)
## 
##     Null deviance: 3.6253  on 152  degrees of freedom
## Residual deviance: 2.6813  on 151  degrees of freedom
## AIC: 892.24
## 
## Number of Fisher Scoring iterations: 4
Model02<-glm((z)~x,family=Gamma(link=log))
cooksd <- cooks.distance(Model02)
influential <- cooksd[(cooksd > (3 * mean(cooksd, na.rm = TRUE)))]
influential=as.numeric(names(influential))
z=z[-influential]
xaux=x[-influential]

Model02<-glm((z)~xaux,family=Gamma(link=log))
summary(Model02)
## 
## Call:
## glm(formula = (z) ~ xaux, family = Gamma(link = log))
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -3.1982  -1.3864  -0.4394   0.3199   1.8593  
## 
## Coefficients:
##                          Estimate Std. Error t value Pr(>|t|)    
## (Intercept)                5.7554     0.1220  47.184   <2e-16 ***
## xauxafter state of alarm   0.0851     0.1719   0.495    0.621    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for Gamma family taken to be 1.145631)
## 
##     Null deviance: 277.58  on 154  degrees of freedom
## Residual deviance: 277.30  on 153  degrees of freedom
## AIC: 2099.3
## 
## Number of Fisher Scoring iterations: 6

2.15 Sardinella aurita

data1=subset(data_A,data_A$specie=="SAA-ALACHA")

Is gamma a correct distribution for the response variable PRICE?

summary(data1$euros)
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##  0.0100  0.2000  0.2333  2.2234  0.2500 49.5013
a=fitdist(data1$euros, distr = "gamma",start=list(shape = 1, rate = 2), lower = -1,method = "mme")
plot(a)

ks.test(na.omit(data1$euros), "pgamma", a$estimate[1],a$estimate[2])
## 
##  One-sample Kolmogorov-Smirnov test
## 
## data:  na.omit(data1$euros)
## D = 0.61861, p-value = 2.187e-14
## alternative hypothesis: two-sided
gamma_test(data1$euros)
## 
##  Test of fit for the Gamma distribution
## 
## data:  data1$euros
## V = 7.3646, p-value = 1.914e-07

Is gamma a correct distribution for the response variable ABUNDANCE?

summary(data1$kgs)
##     Min.  1st Qu.   Median     Mean  3rd Qu.     Max. 
##     1.00    95.25   700.00  4039.50  6875.00 28292.00
a=fitdist(data1$kgs, distr = "gamma",start=list(shape = 1, rate = 2), lower = -1,method = "mme")
plot(a)

ks.test(na.omit(data1$kgs), "pgamma", a$estimate[1],a$estimate[2])
## 
##  One-sample Kolmogorov-Smirnov test
## 
## data:  na.omit(data1$kgs)
## D = 0.18185, p-value = 0.1243
## alternative hypothesis: two-sided
gamma_test(data1$kgs)
## 
##  Test of fit for the Gamma distribution
## 
## data:  data1$kgs
## V = -0.93081, p-value = 0.5104

Regression models

x=as.factor(data1$Covid)
levels(x)


data0=subset(data1,data1$Covid=="Reference state of alarm 1" | data1$Covid=="state of alarm 1")
boxplot(data0$euros~data0$Covid, col = rgb(0, 0.5, 1, alpha = 0.5),xlab="Covid Category",ylab="Price")
boxplot(data0$kgs~data0$Covid, col = rgb(0, 0.5, 1, alpha = 0.5),xlab="Covid Category",ylab="Abundance")
x=as.factor(data0$Covid)
table(x)
y=as.numeric(data0$euros)
x=relevel(x, ref = "Reference state of alarm 1")
z=as.numeric(data0$kgs)

Model0<-glm((y)~x,family=Gamma(link=log))
cooksd <- cooks.distance(Model0)
influential <- cooksd[(cooksd > (3 * mean(cooksd, na.rm = TRUE)))]
influential=as.numeric(names(influential))
y=y[-influential]
xaux=x[-influential]
Model0<-glm((y)~xaux,family=Gamma(link=log))
summary(Model0)

Model0<-glm((z)~x,family=Gamma(link=log))
cooksd <- cooks.distance(Model0)
influential <- cooksd[(cooksd > (3 * mean(cooksd, na.rm = TRUE)))]
influential=as.numeric(names(influential))
z=z[-influential]
xaux=x[-influential]

Model0<-glm((z)~xaux,family=Gamma(link=log))
summary(Model0)

data01=subset(data1,data1$Covid=="Reference state of alarm 2" | data1$Covid=="state of alarm 2")
boxplot(data01$euros~data01$Covid, col = rgb(0, 0.5, 1, alpha = 0.5),xlab="Covid Category",ylab="Price")
boxplot(data01$kgs~data01$Covid, col = rgb(0, 0.5, 1, alpha = 0.5),xlab="Covid Category",ylab="Abundance")
x=as.factor(data01$Covid)
table(x)
y=as.numeric(data01$euros)
x=relevel(x, ref = "Reference state of alarm 2")
z=as.numeric(data01$kgs)


Model01<-glm((y)~x,family=Gamma(link=log))
cooksd <- cooks.distance(Model01)
influential <- cooksd[(cooksd > (3 * mean(cooksd, na.rm = TRUE)))]
influential=as.numeric(names(influential))
y=y[-influential]
xaux=x[-influential]
Model01<-glm((y)~xaux,family=Gamma(link=log))
summary(Model01)

Model01<-glm((z)~x,family=Gamma(link=log))
cooksd <- cooks.distance(Model01)
influential <- cooksd[(cooksd > (3 * mean(cooksd, na.rm = TRUE)))]
influential=as.numeric(names(influential))
z=z[-influential]
xaux=x[-influential]

Model01<-glm((z)~xaux,family=Gamma(link=log))
summary(Model01)


data02=subset(data1,data1$Covid=="Reference after state of alarm" | data1$Covid=="after state of alarm")
boxplot(data02$euros~data02$Covid, col = rgb(0, 0.5, 1, alpha = 0.5),xlab="Covid Category",ylab="Price")
boxplot(data02$kgs~data02$Covid, col = rgb(0, 0.5, 1, alpha = 0.5),xlab="Covid Category",ylab="Abundance")
x=as.factor(data02$Covid)
table(x)
y=as.numeric(data02$euros)
x=relevel(x, ref = "Reference after state of alarm")
z=as.numeric(data02$kgs)

Model02<-glm((y)~x,family=Gamma(link=log))
cooksd <- cooks.distance(Model02)
influential <- cooksd[(cooksd > (3 * mean(cooksd, na.rm = TRUE)))]
influential=as.numeric(names(influential))
y=y[-influential]
xaux=x[-influential]
Model02<-glm((y)~xaux,family=Gamma(link=log))
summary(Model02)

Model02<-glm((z)~x,family=Gamma(link=log))
cooksd <- cooks.distance(Model02)
influential <- cooksd[(cooksd > (3 * mean(cooksd, na.rm = TRUE)))]
influential=as.numeric(names(influential))
z=z[-influential]
xaux=x[-influential]

Model02<-glm((z)~xaux,family=Gamma(link=log))
summary(Model02)

2.16 Thunnus thynnus

data1=subset(data_A,data_A$specie=="BFT-ATUN ROJO" )

Is gamma a correct distribution for the response variable PRICE?

summary(data1$euros)
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##   7.688  10.222  12.906  13.201  15.551  19.950
a=fitdist(data1$euros, distr = "gamma",start=list(shape = 1, rate = 2), lower = -1,method = "mme")
plot(a)

ks.test(na.omit(data1$euros), "pgamma", a$estimate[1],a$estimate[2])
## 
##  One-sample Kolmogorov-Smirnov test
## 
## data:  na.omit(data1$euros)
## D = 0.081369, p-value = 0.281
## alternative hypothesis: two-sided
gamma_test(data1$euros)
## 
##  Test of fit for the Gamma distribution
## 
## data:  data1$euros
## V = -0.38469, p-value = 0.7856

Is gamma a correct distribution for the response variable ABUNDANCE?

summary(data1$kgs)
##     Min.  1st Qu.   Median     Mean  3rd Qu.     Max. 
##     47.0    929.2   3715.5  13644.5  14168.8 297300.0
a=fitdist(data1$kgs, distr = "gamma",start=list(shape = 1, rate = 2), lower = -1,method = "mme")
plot(a)

ks.test(na.omit(data1$kgs), "pgamma", a$estimate[1],a$estimate[2])
## 
##  One-sample Kolmogorov-Smirnov test
## 
## data:  na.omit(data1$kgs)
## D = 0.29088, p-value = 2.656e-11
## alternative hypothesis: two-sided
gamma_test(data1$kgs)
## 
##  Test of fit for the Gamma distribution
## 
## data:  data1$kgs
## V = 8.1915, p-value = 6.944e-09

Regression models

x=as.factor(data1$Covid)
levels(x)
## [1] "after state of alarm"           "Reference after state of alarm"
## [3] "Reference state of alarm 1"     "Reference state of alarm 2"    
## [5] "state of alarm 1"               "state of alarm 2"
data0=subset(data1,data1$Covid=="Reference state of alarm 1" | data1$Covid=="state of alarm 1")
boxplot(data0$euros~data0$Covid, col = rgb(0, 0.5, 1, alpha = 0.5),xlab="Covid Category",ylab="Price")

boxplot(data0$kgs~data0$Covid, col = rgb(0, 0.5, 1, alpha = 0.5),xlab="Covid Category",ylab="Abundance")

x=as.factor(data0$Covid)
table(x)
## x
## Reference state of alarm 1           state of alarm 1 
##                         36                         39
y=as.numeric(data0$euros)
x=relevel(x, ref = "Reference state of alarm 1")
z=as.numeric(data0$kgs)

Model0<-glm((y)~x,family=Gamma(link=log))
cooksd <- cooks.distance(Model0)
influential <- cooksd[(cooksd > (3 * mean(cooksd, na.rm = TRUE)))]
influential=as.numeric(names(influential))
y=y[-influential]
xaux=x[-influential]
Model0<-glm((y)~xaux,family=Gamma(link=log))
summary(Model0)
## 
## Call:
## glm(formula = (y) ~ xaux, family = Gamma(link = log))
## 
## Deviance Residuals: 
##      Min        1Q    Median        3Q       Max  
## -0.34687  -0.20146   0.01516   0.16914   0.30625  
## 
## Coefficients:
##                      Estimate Std. Error t value Pr(>|t|)    
## (Intercept)           2.73835    0.03241  84.479  < 2e-16 ***
## xauxstate of alarm 1 -0.18231    0.04522  -4.032 0.000139 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for Gamma family taken to be 0.03677456)
## 
##     Null deviance: 3.2597  on 71  degrees of freedom
## Residual deviance: 2.6617  on 70  degrees of freedom
## AIC: 351.51
## 
## Number of Fisher Scoring iterations: 4
Model0<-glm((z)~x,family=Gamma(link=log))
cooksd <- cooks.distance(Model0)
influential <- cooksd[(cooksd > (3 * mean(cooksd, na.rm = TRUE)))]
influential=as.numeric(names(influential))
z=z[-influential]
xaux=x[-influential]

Model0<-glm((z)~xaux,family=Gamma(link=log))
summary(Model0)
## 
## Call:
## glm(formula = (z) ~ xaux, family = Gamma(link = log))
## 
## Deviance Residuals: 
##      Min        1Q    Median        3Q       Max  
## -1.82539  -0.98517  -0.41241   0.00846   2.60123  
## 
## Coefficients:
##                      Estimate Std. Error t value Pr(>|t|)    
## (Intercept)            9.0572     0.2253  40.199   <2e-16 ***
## xauxstate of alarm 1  -0.5272     0.3143  -1.677   0.0979 .  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for Gamma family taken to be 1.776788)
## 
##     Null deviance: 84.215  on 71  degrees of freedom
## Residual deviance: 79.248  on 70  degrees of freedom
## AIC: 1416
## 
## Number of Fisher Scoring iterations: 6
data01=subset(data1,data1$Covid=="Reference state of alarm 2" | data1$Covid=="state of alarm 2")
boxplot(data01$euros~data01$Covid, col = rgb(0, 0.5, 1, alpha = 0.5),xlab="Covid Category",ylab="Price")

boxplot(data01$kgs~data01$Covid, col = rgb(0, 0.5, 1, alpha = 0.5),xlab="Covid Category",ylab="Abundance")

x=as.factor(data01$Covid)
table(x)
## x
## Reference state of alarm 2           state of alarm 2 
##                          1                         19
y=as.numeric(data01$euros)
x=relevel(x, ref = "Reference state of alarm 2")
z=as.numeric(data01$kgs)


Model01<-glm((y)~x,family=Gamma(link=log))
cooksd <- cooks.distance(Model01)
influential <- cooksd[(cooksd > (3 * mean(cooksd, na.rm = TRUE)))]
influential=as.numeric(names(influential))[!is.na(as.numeric(names(influential)))]
y=y[-influential]
xaux=x[-influential]
Model01<-glm((y)~xaux,family=Gamma(link=log))
summary(Model01)
## 
## Call:
## glm(formula = (y) ~ xaux, family = Gamma(link = log))
## 
## Deviance Residuals: 
##       Min         1Q     Median         3Q        Max  
## -0.144927  -0.056179   0.008645   0.051247   0.099014  
## 
## Coefficients:
##                      Estimate Std. Error t value Pr(>|t|)    
## (Intercept)           2.35088    0.07650   30.73 1.17e-15 ***
## xauxstate of alarm 2  0.32745    0.07872    4.16 0.000738 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for Gamma family taken to be 0.005852491)
## 
##     Null deviance: 0.187965  on 17  degrees of freedom
## Residual deviance: 0.095922  on 16  degrees of freedom
## AIC: 58.544
## 
## Number of Fisher Scoring iterations: 3
Model01<-glm((z)~x,family=Gamma(link=log))
cooksd <- cooks.distance(Model01)
influential <- cooksd[(cooksd > (3 * mean(cooksd, na.rm = TRUE)))]
influential=as.numeric(names(influential))[!is.na(as.numeric(names(influential)))]
z=z[-influential]
xaux=x[-influential]

Model01<-glm((z)~xaux,family=Gamma(link=log))
summary(Model01)
## 
## Call:
## glm(formula = (z) ~ xaux, family = Gamma(link = log))
## 
## Deviance Residuals: 
##      Min        1Q    Median        3Q       Max  
## -1.97198  -1.01958  -0.02782   0.31640   1.07280  
## 
## Coefficients:
##                      Estimate Std. Error t value Pr(>|t|)    
## (Intercept)            6.5338     0.7626   8.568 1.42e-07 ***
## xauxstate of alarm 2   3.2506     0.7835   4.149 0.000672 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for Gamma family taken to be 0.5815492)
## 
##     Null deviance: 20.534  on 18  degrees of freedom
## Residual deviance: 16.005  on 17  degrees of freedom
## AIC: 408.57
## 
## Number of Fisher Scoring iterations: 5
data02=subset(data1,data1$Covid=="Reference after state of alarm" | data1$Covid=="after state of alarm")
boxplot(data02$euros~data02$Covid, col = rgb(0, 0.5, 1, alpha = 0.5),xlab="Covid Category",ylab="Price")

boxplot(data02$kgs~data02$Covid, col = rgb(0, 0.5, 1, alpha = 0.5),xlab="Covid Category",ylab="Abundance")

x=as.factor(data02$Covid)
table(x)
## x
##           after state of alarm Reference after state of alarm 
##                             22                             31
y=as.numeric(data02$euros)
x=relevel(x, ref = "Reference after state of alarm")
z=as.numeric(data02$kgs)

Model02<-glm((y)~x,family=Gamma(link=log))
cooksd <- cooks.distance(Model02)
influential <- cooksd[(cooksd > (3 * mean(cooksd, na.rm = TRUE)))]
influential=as.numeric(names(influential))
y=y[-influential]
xaux=x[-influential]
Model02<-glm((y)~xaux,family=Gamma(link=log))
summary(Model02)
## 
## Call:
## glm(formula = (y) ~ xaux, family = Gamma(link = log))
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -0.3315  -0.1777  -0.1046   0.1265   0.4797  
## 
## Coefficients:
##                          Estimate Std. Error t value Pr(>|t|)    
## (Intercept)               2.48572    0.04856  51.189   <2e-16 ***
## xauxafter state of alarm -0.05537    0.07393  -0.749    0.457    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for Gamma family taken to be 0.06838266)
## 
##     Null deviance: 3.0189  on 50  degrees of freedom
## Residual deviance: 2.9807  on 49  degrees of freedom
## AIC: 254.53
## 
## Number of Fisher Scoring iterations: 4
Model02<-glm((z)~x,family=Gamma(link=log))
cooksd <- cooks.distance(Model02)
influential <- cooksd[(cooksd > (3 * mean(cooksd, na.rm = TRUE)))]
influential=as.numeric(names(influential))
z=z[-influential]
xaux=x[-influential]

Model02<-glm((z)~xaux,family=Gamma(link=log))
summary(Model02)
## 
## Call:
## glm(formula = (z) ~ xaux, family = Gamma(link = log))
## 
## Deviance Residuals: 
##    Min      1Q  Median      3Q     Max  
## -2.918  -1.967  -1.512  -0.129   3.358  
## 
## Coefficients:
##                          Estimate Std. Error t value Pr(>|t|)    
## (Intercept)                8.1158     0.3359  24.162  < 2e-16 ***
## xauxafter state of alarm   1.6237     0.5114   3.175  0.00259 ** 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for Gamma family taken to be 3.271883)
## 
##     Null deviance: 202.88  on 50  degrees of freedom
## Residual deviance: 170.90  on 49  degrees of freedom
## AIC: 964.94
## 
## Number of Fisher Scoring iterations: 9

2.17 Auxis rochei

data1=subset(data_A,data_A$specie=="BLT-MELVA" )

Is gamma a correct distribution for the response variable PRICE?

summary(data1$euros)
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##  0.3968  1.1664  1.4115  1.8119  2.2550  7.1149
a=fitdist(data1$euros, distr = "gamma",start=list(shape = 1, rate = 2), lower = -1,method = "mme")
plot(a)

ks.test(na.omit(data1$euros), "pgamma", a$estimate[1],a$estimate[2])
## 
##  One-sample Kolmogorov-Smirnov test
## 
## data:  na.omit(data1$euros)
## D = 0.13014, p-value = 0.00916
## alternative hypothesis: two-sided
gamma_test(data1$euros)
## 
##  Test of fit for the Gamma distribution
## 
## data:  data1$euros
## V = 5.4697, p-value = 0.0001099

Is gamma a correct distribution for the response variable ABUNDANCE?

summary(data1$kgs)
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##    1.02    8.13   27.30   87.22   80.82 1010.81
a=fitdist(data1$kgs, distr = "gamma",start=list(shape = 1, rate = 2), lower = -1,method = "mme")
plot(a)

ks.test(na.omit(data1$kgs), "pgamma", a$estimate[1],a$estimate[2])
## 
##  One-sample Kolmogorov-Smirnov test
## 
## data:  na.omit(data1$kgs)
## D = 0.24017, p-value = 2.161e-08
## alternative hypothesis: two-sided
gamma_test(data1$kgs)
## 
##  Test of fit for the Gamma distribution
## 
## data:  data1$kgs
## V = 5.5626, p-value = 8.378e-05

Regression models

x=as.factor(data1$Covid)
levels(x)
## [1] "after state of alarm"           "Reference after state of alarm"
## [3] "Reference No"                   "Reference state of alarm 1"    
## [5] "Reference state of alarm 2"     "state of alarm 1"              
## [7] "state of alarm 2"
data0=subset(data1,data1$Covid=="Reference state of alarm 1" | data1$Covid=="state of alarm 1")
boxplot(data0$euros~data0$Covid, col = rgb(0, 0.5, 1, alpha = 0.5),xlab="Covid Category",ylab="Price")

boxplot(data0$kgs~data0$Covid, col = rgb(0, 0.5, 1, alpha = 0.5),xlab="Covid Category",ylab="Abundance")

x=as.factor(data0$Covid)
table(x)
## x
## Reference state of alarm 1           state of alarm 1 
##                         21                          8
y=as.numeric(data0$euros)
x=relevel(x, ref = "Reference state of alarm 1")
z=as.numeric(data0$kgs)

Model0<-glm((y)~x,family=Gamma(link=log))
cooksd <- cooks.distance(Model0)
influential <- cooksd[(cooksd > (3 * mean(cooksd, na.rm = TRUE)))]
influential=as.numeric(names(influential))
y=y[-influential]
xaux=x[-influential]
Model0<-glm((y)~xaux,family=Gamma(link=log))
summary(Model0)
## 
## Call:
## glm(formula = (y) ~ xaux, family = Gamma(link = log))
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -1.0202  -0.1925  -0.1143   0.1372   0.5583  
## 
## Coefficients:
##                      Estimate Std. Error t value Pr(>|t|)    
## (Intercept)           0.99322    0.07618  13.037 1.19e-12 ***
## xauxstate of alarm 1 -0.55457    0.14962  -3.706  0.00105 ** 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for Gamma family taken to be 0.1160789)
## 
##     Null deviance: 4.5521  on 26  degrees of freedom
## Residual deviance: 3.1029  on 25  degrees of freedom
## AIC: 67.492
## 
## Number of Fisher Scoring iterations: 4
Model0<-glm((z)~x,family=Gamma(link=log))
cooksd <- cooks.distance(Model0)
influential <- cooksd[(cooksd > (3 * mean(cooksd, na.rm = TRUE)))]
influential=as.numeric(names(influential))
z=z[-influential]
xaux=x[-influential]

Model0<-glm((z)~xaux,family=Gamma(link=log))
summary(Model0)
## 
## Call:
## glm(formula = (z) ~ xaux, family = Gamma(link = log))
## 
## Deviance Residuals: 
##      Min        1Q    Median        3Q       Max  
## -1.72332  -1.03617  -0.42145   0.03689   1.84167  
## 
## Coefficients:
##                      Estimate Std. Error t value Pr(>|t|)    
## (Intercept)            2.3433     0.2487   9.422 1.05e-09 ***
## xauxstate of alarm 1   0.2971     0.4885   0.608    0.548    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for Gamma family taken to be 1.237154)
## 
##     Null deviance: 26.313  on 26  degrees of freedom
## Residual deviance: 25.834  on 25  degrees of freedom
## AIC: 190.49
## 
## Number of Fisher Scoring iterations: 5
data01=subset(data1,data1$Covid=="Reference state of alarm 2" | data1$Covid=="state of alarm 2")
boxplot(data01$euros~data01$Covid, col = rgb(0, 0.5, 1, alpha = 0.5),xlab="Covid Category",ylab="Price")

boxplot(data01$kgs~data01$Covid, col = rgb(0, 0.5, 1, alpha = 0.5),xlab="Covid Category",ylab="Abundance")

x=as.factor(data01$Covid)
table(x)
## x
## Reference state of alarm 2           state of alarm 2 
##                          9                         11
y=as.numeric(data01$euros)
x=relevel(x, ref = "Reference state of alarm 2")
z=as.numeric(data01$kgs)


Model01<-glm((y)~x,family=Gamma(link=log))
cooksd <- cooks.distance(Model01)
influential <- cooksd[(cooksd > (3 * mean(cooksd, na.rm = TRUE)))]
influential=as.numeric(names(influential))
y=y[-influential]
xaux=x[-influential]
Model01<-glm((y)~xaux,family=Gamma(link=log))
summary(Model01)
## 
## Call:
## glm(formula = (y) ~ xaux, family = Gamma(link = log))
## 
## Deviance Residuals: 
##      Min        1Q    Median        3Q       Max  
## -1.05422  -0.27198  -0.03429   0.21184   0.54955  
## 
## Coefficients:
##                      Estimate Std. Error t value Pr(>|t|)  
## (Intercept)           0.29401    0.13380   2.197   0.0431 *
## xauxstate of alarm 2  0.05848    0.17951   0.326   0.7488  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for Gamma family taken to be 0.1432182)
## 
##     Null deviance: 2.7005  on 17  degrees of freedom
## Residual deviance: 2.6853  on 16  degrees of freedom
## AIC: 32.351
## 
## Number of Fisher Scoring iterations: 4
Model01<-glm((z)~x,family=Gamma(link=log))
cooksd <- cooks.distance(Model01)
influential <- cooksd[(cooksd > (3 * mean(cooksd, na.rm = TRUE)))]
influential=as.numeric(names(influential))
z=z[-influential]
xaux=x[-influential]

Model01<-glm((z)~xaux,family=Gamma(link=log))
summary(Model01)
## 
## Call:
## glm(formula = (z) ~ xaux, family = Gamma(link = log))
## 
## Deviance Residuals: 
##      Min        1Q    Median        3Q       Max  
## -1.82458  -0.75914   0.02235   0.25960   1.45408  
## 
## Coefficients:
##                      Estimate Std. Error t value Pr(>|t|)    
## (Intercept)            2.9402     0.2811  10.459 1.47e-08 ***
## xauxstate of alarm 2  -0.8817     0.3772  -2.338   0.0327 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for Gamma family taken to be 0.6322249)
## 
##     Null deviance: 17.120  on 17  degrees of freedom
## Residual deviance: 13.663  on 16  degrees of freedom
## AIC: 128.91
## 
## Number of Fisher Scoring iterations: 5
data02=subset(data1,data1$Covid=="Reference after state of alarm" | data1$Covid=="after state of alarm")
boxplot(data02$euros~data02$Covid, col = rgb(0, 0.5, 1, alpha = 0.5),xlab="Covid Category",ylab="Price")

boxplot(data02$kgs~data02$Covid, col = rgb(0, 0.5, 1, alpha = 0.5),xlab="Covid Category",ylab="Abundance")

x=as.factor(data02$Covid)
table(x)
## x
##           after state of alarm Reference after state of alarm 
##                             39                             64
y=as.numeric(data02$euros)
x=relevel(x, ref = "Reference after state of alarm")
z=as.numeric(data02$kgs)

Model02<-glm((y)~x,family=Gamma(link=log))
cooksd <- cooks.distance(Model02)
influential <- cooksd[(cooksd > (3 * mean(cooksd, na.rm = TRUE)))]
influential=as.numeric(names(influential))
y=y[-influential]
xaux=x[-influential]
Model02<-glm((y)~xaux,family=Gamma(link=log))
summary(Model02)
## 
## Call:
## glm(formula = (y) ~ xaux, family = Gamma(link = log))
## 
## Deviance Residuals: 
##      Min        1Q    Median        3Q       Max  
## -0.65600  -0.21949  -0.01287   0.09566   0.70467  
## 
## Coefficients:
##                          Estimate Std. Error t value Pr(>|t|)    
## (Intercept)               0.20316    0.03535   5.747 1.12e-07 ***
## xauxafter state of alarm  0.28337    0.05619   5.043 2.22e-06 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for Gamma family taken to be 0.072488)
## 
##     Null deviance: 8.2514  on 95  degrees of freedom
## Residual deviance: 6.3772  on 94  degrees of freedom
## AIC: 73.35
## 
## Number of Fisher Scoring iterations: 4
Model02<-glm((z)~x,family=Gamma(link=log))
cooksd <- cooks.distance(Model02)
influential <- cooksd[(cooksd > (3 * mean(cooksd, na.rm = TRUE)))]
influential=as.numeric(names(influential))
z=z[-influential]
xaux=x[-influential]

Model02<-glm((z)~xaux,family=Gamma(link=log))
summary(Model02)
## 
## Call:
## glm(formula = (z) ~ xaux, family = Gamma(link = log))
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -2.6174  -1.2001  -0.3292   0.2830   2.1121  
## 
## Coefficients:
##                          Estimate Std. Error t value Pr(>|t|)    
## (Intercept)                4.6679     0.1409  33.123  < 2e-16 ***
## xauxafter state of alarm  -0.9885     0.2270  -4.355 3.39e-05 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for Gamma family taken to be 1.171705)
## 
##     Null deviance: 141.56  on 95  degrees of freedom
## Residual deviance: 121.61  on 94  degrees of freedom
## AIC: 1022.1
## 
## Number of Fisher Scoring iterations: 6

3 Mediterranean

3.1 Octopus vulgaris

data1=subset(data_M,data_M$specie=="OCC-PULPO DE ROCA O PULPO ROQUERO")

Is gamma a correct distribution for the response variable PRICE?

summary(data1$euros)
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##   3.973   5.784   6.188   6.318   6.846  10.000
a=fitdist(data1$euros, distr = "gamma",start=list(shape = 1, rate = 2), lower = -1,method = "mme")
plot(a)

ks.test(na.omit(data1$euros), "pgamma", a$estimate[1],a$estimate[2])
## 
##  One-sample Kolmogorov-Smirnov test
## 
## data:  na.omit(data1$euros)
## D = 0.060044, p-value = 0.02084
## alternative hypothesis: two-sided
gamma_test(data1$euros)
## 
##  Test of fit for the Gamma distribution
## 
## data:  data1$euros
## V = 3.5197, p-value = 0.01282

Is gamma a correct distribution for the response variable ABUNDANCE?

summary(data1$kgs)
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##     2.4  1328.8  4405.4  4124.3  6176.6 13266.7
a=fitdist(data1$kgs, distr = "gamma",start=list(shape = 1, rate = 2), lower = -1,method = "mme")
plot(a)

ks.test(na.omit(data1$kgs), "pgamma", a$estimate[1],a$estimate[2])
## 
##  One-sample Kolmogorov-Smirnov test
## 
## data:  na.omit(data1$kgs)
## D = 0.14301, p-value = 1.14e-11
## alternative hypothesis: two-sided
gamma_test(data1$kgs)
## 
##  Test of fit for the Gamma distribution
## 
## data:  data1$kgs
## V = -11.632, p-value < 2.2e-16

Regression models

x=as.factor(data1$Covid)
levels(x)
## [1] "after state of alarm"           "Reference after state of alarm"
## [3] "Reference No"                   "Reference state of alarm 1"    
## [5] "Reference state of alarm 2"     "state of alarm 1"              
## [7] "state of alarm 2"
data0=subset(data1,data1$Covid=="Reference state of alarm 1" | data1$Covid=="state of alarm 1")
boxplot(data0$euros~data0$Covid, col = rgb(0, 0.5, 1, alpha = 0.5),xlab="Covid Category",ylab="Price")

boxplot(data0$kgs~data0$Covid, col = rgb(0, 0.5, 1, alpha = 0.5),xlab="Covid Category",ylab="Abundance")

x=as.factor(data0$Covid)
table(x)
## x
## Reference state of alarm 1           state of alarm 1 
##                         73                         72
y=as.numeric(data0$euros)
x=relevel(x, ref = "Reference state of alarm 1")
z=as.numeric(data0$kgs)

Model0<-glm((y)~x,family=Gamma(link=log))
cooksd <- cooks.distance(Model0)
influential <- cooksd[(cooksd > (3 * mean(cooksd, na.rm = TRUE)))]
influential=as.numeric(names(influential))
y=y[-influential]
xaux=x[-influential]
Model0<-glm((y)~xaux,family=Gamma(link=log))
summary(Model0)
## 
## Call:
## glm(formula = (y) ~ xaux, family = Gamma(link = log))
## 
## Deviance Residuals: 
##      Min        1Q    Median        3Q       Max  
## -0.12035  -0.04469  -0.00082   0.04019   0.14068  
## 
## Coefficients:
##                       Estimate Std. Error t value Pr(>|t|)    
## (Intercept)           1.781674   0.006449  276.27   <2e-16 ***
## xauxstate of alarm 1 -0.155192   0.009401  -16.51   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for Gamma family taken to be 0.002994371)
## 
##     Null deviance: 1.21243  on 135  degrees of freedom
## Residual deviance: 0.39968  on 134  degrees of freedom
## AIC: 63.525
## 
## Number of Fisher Scoring iterations: 3
Model0<-glm((z)~x,family=Gamma(link=log))
cooksd <- cooks.distance(Model0)
influential <- cooksd[(cooksd > (3 * mean(cooksd, na.rm = TRUE)))]
influential=as.numeric(names(influential))
z=z[-influential]
xaux=x[-influential]

Model0<-glm((z)~xaux,family=Gamma(link=log))
summary(Model0)
## 
## Call:
## glm(formula = (z) ~ xaux, family = Gamma(link = log))
## 
## Deviance Residuals: 
##      Min        1Q    Median        3Q       Max  
## -1.25766  -0.35934   0.04191   0.31800   0.63146  
## 
## Coefficients:
##                      Estimate Std. Error t value Pr(>|t|)    
## (Intercept)           8.69102    0.04913 176.911   <2e-16 ***
## xauxstate of alarm 1 -0.10890    0.07054  -1.544    0.125    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for Gamma family taken to be 0.1665252)
## 
##     Null deviance: 29.051  on 133  degrees of freedom
## Residual deviance: 28.654  on 132  degrees of freedom
## AIC: 2470.7
## 
## Number of Fisher Scoring iterations: 4
data01=subset(data1,data1$Covid=="Reference state of alarm 2" | data1$Covid=="state of alarm 2")
boxplot(data01$euros~data01$Covid, col = rgb(0, 0.5, 1, alpha = 0.5),xlab="Covid Category",ylab="Price")

boxplot(data01$kgs~data01$Covid, col = rgb(0, 0.5, 1, alpha = 0.5),xlab="Covid Category",ylab="Abundance")

x=as.factor(data01$Covid)
table(x)
## x
## Reference state of alarm 2           state of alarm 2 
##                        103                        144
y=as.numeric(data01$euros)
x=relevel(x, ref = "Reference state of alarm 2")
z=as.numeric(data01$kgs)


Model01<-glm((y)~x,family=Gamma(link=log))
cooksd <- cooks.distance(Model01)
influential <- cooksd[(cooksd > (3 * mean(cooksd, na.rm = TRUE)))]
influential=as.numeric(names(influential))
y=y[-influential]
xaux=x[-influential]
Model01<-glm((y)~xaux,family=Gamma(link=log))
summary(Model01)
## 
## Call:
## glm(formula = (y) ~ xaux, family = Gamma(link = log))
## 
## Deviance Residuals: 
##       Min         1Q     Median         3Q        Max  
## -0.191353  -0.046391   0.001734   0.049823   0.163427  
## 
## Coefficients:
##                      Estimate Std. Error t value Pr(>|t|)    
## (Intercept)          1.825669   0.007361 248.027  < 2e-16 ***
## xauxstate of alarm 2 0.034397   0.009792   3.513 0.000538 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for Gamma family taken to be 0.005255544)
## 
##     Null deviance: 1.2417  on 222  degrees of freedom
## Residual deviance: 1.1769  on 221  degrees of freedom
## AIC: 291.32
## 
## Number of Fisher Scoring iterations: 3
Model01<-glm((z)~x,family=Gamma(link=log))
cooksd <- cooks.distance(Model01)
influential <- cooksd[(cooksd > (3 * mean(cooksd, na.rm = TRUE)))]
influential=as.numeric(names(influential))
z=z[-influential]
xaux=x[-influential]

Model01<-glm((z)~xaux,family=Gamma(link=log))
summary(Model01)
## 
## Call:
## glm(formula = (z) ~ xaux, family = Gamma(link = log))
## 
## Deviance Residuals: 
##      Min        1Q    Median        3Q       Max  
## -2.63928  -0.27956   0.04822   0.26808   0.76224  
## 
## Coefficients:
##                       Estimate Std. Error t value Pr(>|t|)    
## (Intercept)           8.490809   0.048875 173.727   <2e-16 ***
## xauxstate of alarm 2 -0.002064   0.063327  -0.033    0.974    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for Gamma family taken to be 0.2221507)
## 
##     Null deviance: 106.99  on 229  degrees of freedom
## Residual deviance: 106.99  on 228  degrees of freedom
## AIC: 4298.6
## 
## Number of Fisher Scoring iterations: 5
data02=subset(data1,data1$Covid=="Reference after state of alarm" | data1$Covid=="after state of alarm")
boxplot(data02$euros~data02$Covid, col = rgb(0, 0.5, 1, alpha = 0.5),xlab="Covid Category",ylab="Price")

boxplot(data02$kgs~data02$Covid, col = rgb(0, 0.5, 1, alpha = 0.5),xlab="Covid Category",ylab="Abundance")

x=as.factor(data02$Covid)
table(x)
## x
##           after state of alarm Reference after state of alarm 
##                             94                             92
y=as.numeric(data02$euros)
x=relevel(x, ref = "Reference after state of alarm")
z=as.numeric(data02$kgs)

Model02<-glm((y)~x,family=Gamma(link=log))
cooksd <- cooks.distance(Model02)
influential <- cooksd[(cooksd > (3 * mean(cooksd, na.rm = TRUE)))]
influential=as.numeric(names(influential))
y=y[-influential]
xaux=x[-influential]
Model02<-glm((y)~xaux,family=Gamma(link=log))
summary(Model02)
## 
## Call:
## glm(formula = (y) ~ xaux, family = Gamma(link = log))
## 
## Deviance Residuals: 
##      Min        1Q    Median        3Q       Max  
## -0.23619  -0.09891   0.01059   0.09190   0.19907  
## 
## Coefficients:
##                          Estimate Std. Error t value Pr(>|t|)    
## (Intercept)               1.92905    0.01280 150.718   <2e-16 ***
## xauxafter state of alarm  0.01597    0.01790   0.892    0.374    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for Gamma family taken to be 0.01441592)
## 
##     Null deviance: 2.6491  on 179  degrees of freedom
## Residual deviance: 2.6376  on 178  degrees of freedom
## AIC: 451.86
## 
## Number of Fisher Scoring iterations: 4
Model02<-glm((z)~x,family=Gamma(link=log))
cooksd <- cooks.distance(Model02)
influential <- cooksd[(cooksd > (3 * mean(cooksd, na.rm = TRUE)))]
influential=as.numeric(names(influential))
z=z[-influential]
xaux=x[-influential]

Model02<-glm((z)~xaux,family=Gamma(link=log))
summary(Model02)
## 
## Call:
## glm(formula = (z) ~ xaux, family = Gamma(link = log))
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -3.0557  -0.8306  -0.4666   0.1162   1.7821  
## 
## Coefficients:
##                          Estimate Std. Error t value Pr(>|t|)    
## (Intercept)                7.4231     0.1115  66.553   <2e-16 ***
## xauxafter state of alarm  -0.1545     0.1568  -0.985    0.326    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for Gamma family taken to be 1.032558)
## 
##     Null deviance: 173.09  on 167  degrees of freedom
## Residual deviance: 172.09  on 166  degrees of freedom
## AIC: 2810.5
## 
## Number of Fisher Scoring iterations: 5

3.2 Engraulis encrasicolus

data1=subset(data_M,data_M$specie=="ANE-BOQUERON")

Is gamma a correct distribution for the response variable PRICE?

summary(data1$euros)
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##   0.000   2.018   2.950   3.163   4.015  11.010
a=fitdist(data1$euros, distr = "gamma",start=list(shape = 1, rate = 2), lower = -1,method = "mme")
plot(a)

ks.test(na.omit(data1$euros), "pgamma", a$estimate[1],a$estimate[2])
## 
##  One-sample Kolmogorov-Smirnov test
## 
## data:  na.omit(data1$euros)
## D = 0.020647, p-value = 0.8103
## alternative hypothesis: two-sided
gamma_test(data1$euros)
## 
##  Test of fit for the Gamma distribution
## 
## data:  data1$euros
## V = NaN, p-value = NA

Is gamma a correct distribution for the response variable ABUNDANCE?

summary(data1$kgs)
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##     1.6   641.8  3680.6  7882.5 11334.5 80598.1
a=fitdist(data1$kgs, distr = "gamma",start=list(shape = 1, rate = 2), lower = -1,method = "mme")
plot(a)

ks.test(na.omit(data1$kgs), "pgamma", a$estimate[1],a$estimate[2])
## 
##  One-sample Kolmogorov-Smirnov test
## 
## data:  na.omit(data1$kgs)
## D = 0.072767, p-value = 8.106e-05
## alternative hypothesis: two-sided
gamma_test(data1$kgs)
## 
##  Test of fit for the Gamma distribution
## 
## data:  data1$kgs
## V = -3.3004, p-value = 0.01961

Regression models

ind=which(data1$euros==0)
if(length(ind)>0){data1=data1[-ind,]}

x=as.factor(data1$Covid)
levels(x)
## [1] "after state of alarm"           "Reference after state of alarm"
## [3] "Reference No"                   "Reference state of alarm 1"    
## [5] "Reference state of alarm 2"     "state of alarm 1"              
## [7] "state of alarm 2"
data0=subset(data1,data1$Covid=="Reference state of alarm 1" | data1$Covid=="state of alarm 1")
boxplot(data0$euros~data0$Covid, col = rgb(0, 0.5, 1, alpha = 0.5),xlab="Covid Category",ylab="Price")

boxplot(data0$kgs~data0$Covid, col = rgb(0, 0.5, 1, alpha = 0.5),xlab="Covid Category",ylab="Abundance")

x=as.factor(data0$Covid)
table(x)
## x
## Reference state of alarm 1           state of alarm 1 
##                         62                         46
y=as.numeric(data0$euros)
x=relevel(x, ref = "Reference state of alarm 1")
z=as.numeric(data0$kgs)

Model0<-glm((y)~x,family=Gamma(link=log))
cooksd <- cooks.distance(Model0)
influential <- cooksd[(cooksd > (3 * mean(cooksd, na.rm = TRUE)))]
influential=as.numeric(names(influential))
y=y[-influential]
xaux=x[-influential]
Model0<-glm((y)~xaux,family=Gamma(link=log))
summary(Model0)
## 
## Call:
## glm(formula = (y) ~ xaux, family = Gamma(link = log))
## 
## Deviance Residuals: 
##      Min        1Q    Median        3Q       Max  
## -0.80808  -0.26451  -0.03176   0.25467   0.52399  
## 
## Coefficients:
##                      Estimate Std. Error t value Pr(>|t|)    
## (Intercept)           1.03749    0.04533  22.890   <2e-16 ***
## xauxstate of alarm 1 -0.01716    0.06790  -0.253    0.801    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for Gamma family taken to be 0.1150453)
## 
##     Null deviance: 12.713  on 100  degrees of freedom
## Residual deviance: 12.705  on  99  degrees of freedom
## AIC: 280.68
## 
## Number of Fisher Scoring iterations: 4
Model0<-glm((z)~x,family=Gamma(link=log))
cooksd <- cooks.distance(Model0)
influential <- cooksd[(cooksd > (3 * mean(cooksd, na.rm = TRUE)))]
influential=as.numeric(names(influential))
z=z[-influential]
xaux=x[-influential]

Model0<-glm((z)~xaux,family=Gamma(link=log))
summary(Model0)
## 
## Call:
## glm(formula = (z) ~ xaux, family = Gamma(link = log))
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -3.8489  -1.3561  -0.2949   0.4990   1.4516  
## 
## Coefficients:
##                       Estimate Std. Error t value Pr(>|t|)    
## (Intercept)           9.089996   0.129131  70.393   <2e-16 ***
## xauxstate of alarm 1 -0.001717   0.199856  -0.009    0.993    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for Gamma family taken to be 1.000495)
## 
##     Null deviance: 187.7  on 102  degrees of freedom
## Residual deviance: 187.7  on 101  degrees of freedom
## AIC: 2073.9
## 
## Number of Fisher Scoring iterations: 7
data01=subset(data1,data1$Covid=="Reference state of alarm 2" | data1$Covid=="state of alarm 2")
boxplot(data01$euros~data01$Covid, col = rgb(0, 0.5, 1, alpha = 0.5),xlab="Covid Category",ylab="Price")

boxplot(data01$kgs~data01$Covid, col = rgb(0, 0.5, 1, alpha = 0.5),xlab="Covid Category",ylab="Abundance")

x=as.factor(data01$Covid)
table(x)
## x
## Reference state of alarm 2           state of alarm 2 
##                         82                        102
y=as.numeric(data01$euros)
x=relevel(x, ref = "Reference state of alarm 2")
z=as.numeric(data01$kgs)


Model01<-glm((y)~x,family=Gamma(link=log))
cooksd <- cooks.distance(Model01)
influential <- cooksd[(cooksd > (3 * mean(cooksd, na.rm = TRUE)))]
influential=as.numeric(names(influential))
y=y[-influential]
xaux=x[-influential]
Model01<-glm((y)~xaux,family=Gamma(link=log))
summary(Model01)
## 
## Call:
## glm(formula = (y) ~ xaux, family = Gamma(link = log))
## 
## Deviance Residuals: 
##      Min        1Q    Median        3Q       Max  
## -1.51438  -0.39923  -0.03821   0.34049   0.86772  
## 
## Coefficients:
##                      Estimate Std. Error t value Pr(>|t|)    
## (Intercept)           0.99579    0.05430  18.340   <2e-16 ***
## xauxstate of alarm 2  0.01278    0.07272   0.176    0.861    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for Gamma family taken to be 0.2269999)
## 
##     Null deviance: 49.123  on 173  degrees of freedom
## Residual deviance: 49.116  on 172  degrees of freedom
## AIC: 587.77
## 
## Number of Fisher Scoring iterations: 5
Model01<-glm((z)~x,family=Gamma(link=log))
cooksd <- cooks.distance(Model01)
influential <- cooksd[(cooksd > (3 * mean(cooksd, na.rm = TRUE)))]
influential=as.numeric(names(influential))
z=z[-influential]
xaux=x[-influential]

Model01<-glm((z)~xaux,family=Gamma(link=log))
summary(Model01)
## 
## Call:
## glm(formula = (z) ~ xaux, family = Gamma(link = log))
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -3.8000  -1.2816  -0.3583   0.5098   1.3622  
## 
## Coefficients:
##                      Estimate Std. Error t value Pr(>|t|)    
## (Intercept)            8.7346     0.1056   82.74  < 2e-16 ***
## xauxstate of alarm 2   0.4173     0.1410    2.96  0.00351 ** 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for Gamma family taken to be 0.8468987)
## 
##     Null deviance: 307.67  on 172  degrees of freedom
## Residual deviance: 300.42  on 171  degrees of freedom
## AIC: 3441.9
## 
## Number of Fisher Scoring iterations: 6
data02=subset(data1,data1$Covid=="Reference after state of alarm" | data1$Covid=="after state of alarm")
boxplot(data02$euros~data02$Covid, col = rgb(0, 0.5, 1, alpha = 0.5),xlab="Covid Category",ylab="Price")

boxplot(data02$kgs~data02$Covid, col = rgb(0, 0.5, 1, alpha = 0.5),xlab="Covid Category",ylab="Abundance")

x=as.factor(data02$Covid)
table(x)
## x
##           after state of alarm Reference after state of alarm 
##                             78                             85
y=as.numeric(data02$euros)
x=relevel(x, ref = "Reference after state of alarm")
z=as.numeric(data02$kgs)

Model02<-glm((y)~x,family=Gamma(link=log))
cooksd <- cooks.distance(Model02)
influential <- cooksd[(cooksd > (3 * mean(cooksd, na.rm = TRUE)))]
influential=as.numeric(names(influential))
y=y[-influential]
xaux=x[-influential]
Model02<-glm((y)~xaux,family=Gamma(link=log))
summary(Model02)
## 
## Call:
## glm(formula = (y) ~ xaux, family = Gamma(link = log))
## 
## Deviance Residuals: 
##      Min        1Q    Median        3Q       Max  
## -1.10381  -0.32699  -0.04062   0.30091   0.63865  
## 
## Coefficients:
##                          Estimate Std. Error t value Pr(>|t|)    
## (Intercept)               1.12480    0.04158  27.052  < 2e-16 ***
## xauxafter state of alarm -0.31951    0.06147  -5.198 6.45e-07 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for Gamma family taken to be 0.143494)
## 
##     Null deviance: 29.064  on 152  degrees of freedom
## Residual deviance: 25.238  on 151  degrees of freedom
## AIC: 442.9
## 
## Number of Fisher Scoring iterations: 4
Model02<-glm((z)~x,family=Gamma(link=log))
cooksd <- cooks.distance(Model02)
influential <- cooksd[(cooksd > (3 * mean(cooksd, na.rm = TRUE)))]
influential=as.numeric(names(influential))
z=z[-influential]
xaux=x[-influential]

Model02<-glm((z)~xaux,family=Gamma(link=log))
summary(Model02)
## 
## Call:
## glm(formula = (z) ~ xaux, family = Gamma(link = log))
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -3.9527  -1.0796  -0.3551   0.5073   1.7364  
## 
## Coefficients:
##                          Estimate Std. Error t value Pr(>|t|)    
## (Intercept)                9.4848     0.1080  87.844  < 2e-16 ***
## xauxafter state of alarm  -0.5515     0.1581  -3.489 0.000639 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for Gamma family taken to be 0.9326578)
## 
##     Null deviance: 293.44  on 149  degrees of freedom
## Residual deviance: 282.36  on 148  degrees of freedom
## AIC: 3056.1
## 
## Number of Fisher Scoring iterations: 7

3.3 Sarda sarda

data1=subset(data_M,data_M$specie=="BON-BONITO O BONITO DEL SUR")

Is gamma a correct distribution for the response variable PRICE?

summary(data1$euros)
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##   1.200   4.163   4.990   5.009   5.831   8.620
a=fitdist(data1$euros, distr = "gamma",start=list(shape = 1, rate = 2), lower = -1,method = "mme")
plot(a)

ks.test(na.omit(data1$euros), "pgamma", a$estimate[1],a$estimate[2])
## 
##  One-sample Kolmogorov-Smirnov test
## 
## data:  na.omit(data1$euros)
## D = 0.032006, p-value = 0.599
## alternative hypothesis: two-sided
gamma_test(data1$euros)
## 
##  Test of fit for the Gamma distribution
## 
## data:  data1$euros
## V = -5.8208, p-value = 3.856e-05

Is gamma a correct distribution for the response variable ABUNDANCE?

summary(data1$kgs)
##     Min.  1st Qu.   Median     Mean  3rd Qu.     Max. 
##     0.75    16.92    70.55   393.17   319.32 25150.38
a=fitdist(data1$kgs, distr = "gamma",start=list(shape = 1, rate = 2), lower = -1,method = "mme")
plot(a)

ks.test(na.omit(data1$kgs), "pgamma", a$estimate[1],a$estimate[2])
## 
##  One-sample Kolmogorov-Smirnov test
## 
## data:  na.omit(data1$kgs)
## D = 0.5023, p-value < 2.2e-16
## alternative hypothesis: two-sided
gamma_test(data1$kgs)
## 
##  Test of fit for the Gamma distribution
## 
## data:  data1$kgs
## V = 35.017, p-value < 2.2e-16

Regression models

x=as.factor(data1$Covid)
levels(x)
## [1] "after state of alarm"           "Reference after state of alarm"
## [3] "Reference No"                   "Reference state of alarm 1"    
## [5] "Reference state of alarm 2"     "state of alarm 1"              
## [7] "state of alarm 2"
data0=subset(data1,data1$Covid=="Reference state of alarm 1" | data1$Covid=="state of alarm 1")
boxplot(data0$euros~data0$Covid, col = rgb(0, 0.5, 1, alpha = 0.5),xlab="Covid Category",ylab="Price")

boxplot(data0$kgs~data0$Covid, col = rgb(0, 0.5, 1, alpha = 0.5),xlab="Covid Category",ylab="Abundance")

x=as.factor(data0$Covid)
table(x)
## x
## Reference state of alarm 1           state of alarm 1 
##                         67                         68
y=as.numeric(data0$euros)
x=relevel(x, ref = "Reference state of alarm 1")
z=as.numeric(data0$kgs)

Model0<-glm((y)~x,family=Gamma(link=log))
cooksd <- cooks.distance(Model0)
influential <- cooksd[(cooksd > (3 * mean(cooksd, na.rm = TRUE)))]
influential=as.numeric(names(influential))
y=y[-influential]
xaux=x[-influential]
Model0<-glm((y)~xaux,family=Gamma(link=log))
summary(Model0)
## 
## Call:
## glm(formula = (y) ~ xaux, family = Gamma(link = log))
## 
## Deviance Residuals: 
##      Min        1Q    Median        3Q       Max  
## -0.50470  -0.14140   0.01043   0.11425   0.42314  
## 
## Coefficients:
##                      Estimate Std. Error t value Pr(>|t|)    
## (Intercept)           1.60420    0.02663  60.230   <2e-16 ***
## xauxstate of alarm 1 -0.03658    0.03863  -0.947    0.346    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for Gamma family taken to be 0.04540101)
## 
##     Null deviance: 5.6419  on 121  degrees of freedom
## Residual deviance: 5.6012  on 120  degrees of freedom
## AIC: 358.85
## 
## Number of Fisher Scoring iterations: 4
Model0<-glm((z)~x,family=Gamma(link=log))
cooksd <- cooks.distance(Model0)
influential <- cooksd[(cooksd > (3 * mean(cooksd, na.rm = TRUE)))]
influential=as.numeric(names(influential))
z=z[-influential]
xaux=x[-influential]

Model0<-glm((z)~xaux,family=Gamma(link=log))
summary(Model0)
## 
## Call:
## glm(formula = (z) ~ xaux, family = Gamma(link = log))
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -3.0734  -1.7771  -0.4881   0.3370   2.6349  
## 
## Coefficients:
##                      Estimate Std. Error t value Pr(>|t|)    
## (Intercept)           5.39147    0.15996  33.704   <2e-16 ***
## xauxstate of alarm 1  0.04041    0.22622   0.179    0.859    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for Gamma family taken to be 1.637644)
## 
##     Null deviance: 296.44  on 127  degrees of freedom
## Residual deviance: 296.38  on 126  degrees of freedom
## AIC: 1610.3
## 
## Number of Fisher Scoring iterations: 7
data01=subset(data1,data1$Covid=="Reference state of alarm 2" | data1$Covid=="state of alarm 2")
boxplot(data01$euros~data01$Covid, col = rgb(0, 0.5, 1, alpha = 0.5),xlab="Covid Category",ylab="Price")

boxplot(data01$kgs~data01$Covid, col = rgb(0, 0.5, 1, alpha = 0.5),xlab="Covid Category",ylab="Abundance")

x=as.factor(data01$Covid)
table(x)
## x
## Reference state of alarm 2           state of alarm 2 
##                         90                        129
y=as.numeric(data01$euros)
x=relevel(x, ref = "Reference state of alarm 2")
z=as.numeric(data01$kgs)


Model01<-glm((y)~x,family=Gamma(link=log))
cooksd <- cooks.distance(Model01)
influential <- cooksd[(cooksd > (3 * mean(cooksd, na.rm = TRUE)))]
influential=as.numeric(names(influential))
y=y[-influential]
xaux=x[-influential]
Model01<-glm((y)~xaux,family=Gamma(link=log))
summary(Model01)
## 
## Call:
## glm(formula = (y) ~ xaux, family = Gamma(link = log))
## 
## Deviance Residuals: 
##      Min        1Q    Median        3Q       Max  
## -0.42161  -0.14268   0.02053   0.12305   0.35919  
## 
## Coefficients:
##                      Estimate Std. Error t value Pr(>|t|)    
## (Intercept)           1.61735    0.01989  81.306  < 2e-16 ***
## xauxstate of alarm 2  0.11309    0.02545   4.443 1.46e-05 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for Gamma family taken to be 0.03126017)
## 
##     Null deviance: 7.2224  on 202  degrees of freedom
## Residual deviance: 6.6107  on 201  degrees of freedom
## AIC: 566.09
## 
## Number of Fisher Scoring iterations: 4
Model01<-glm((z)~x,family=Gamma(link=log))
cooksd <- cooks.distance(Model01)
influential <- cooksd[(cooksd > (3 * mean(cooksd, na.rm = TRUE)))]
influential=as.numeric(names(influential))
z=z[-influential]
xaux=x[-influential]

Model01<-glm((z)~xaux,family=Gamma(link=log))
summary(Model01)
## 
## Call:
## glm(formula = (z) ~ xaux, family = Gamma(link = log))
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -2.7716  -1.4316  -0.7901   0.1635   3.1035  
## 
## Coefficients:
##                      Estimate Std. Error t value Pr(>|t|)    
## (Intercept)           4.64643    0.15483  30.010   <2e-16 ***
## xauxstate of alarm 2 -0.03659    0.20202  -0.181    0.856    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for Gamma family taken to be 2.037565)
## 
##     Null deviance: 392.83  on 205  degrees of freedom
## Residual deviance: 392.77  on 204  degrees of freedom
## AIC: 2296.8
## 
## Number of Fisher Scoring iterations: 7
data02=subset(data1,data1$Covid=="Reference after state of alarm" | data1$Covid=="after state of alarm")
boxplot(data02$euros~data02$Covid, col = rgb(0, 0.5, 1, alpha = 0.5),xlab="Covid Category",ylab="Price")

boxplot(data02$kgs~data02$Covid, col = rgb(0, 0.5, 1, alpha = 0.5),xlab="Covid Category",ylab="Abundance")

x=as.factor(data02$Covid)
table(x)
## x
##           after state of alarm Reference after state of alarm 
##                             82                             88
y=as.numeric(data02$euros)
x=relevel(x, ref = "Reference after state of alarm")
z=as.numeric(data02$kgs)

Model02<-glm((y)~x,family=Gamma(link=log))
cooksd <- cooks.distance(Model02)
influential <- cooksd[(cooksd > (3 * mean(cooksd, na.rm = TRUE)))]
influential=as.numeric(names(influential))
y=y[-influential]
xaux=x[-influential]
Model02<-glm((y)~xaux,family=Gamma(link=log))
summary(Model02)
## 
## Call:
## glm(formula = (y) ~ xaux, family = Gamma(link = log))
## 
## Deviance Residuals: 
##      Min        1Q    Median        3Q       Max  
## -0.58233  -0.15373   0.00493   0.12802   0.44724  
## 
## Coefficients:
##                           Estimate Std. Error t value Pr(>|t|)    
## (Intercept)               1.500675   0.024434  61.417   <2e-16 ***
## xauxafter state of alarm -0.005236   0.034785  -0.151    0.881    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for Gamma family taken to be 0.04597193)
## 
##     Null deviance: 7.2721  on 151  degrees of freedom
## Residual deviance: 7.2711  on 150  degrees of freedom
## AIC: 424.64
## 
## Number of Fisher Scoring iterations: 4
Model02<-glm((z)~x,family=Gamma(link=log))
cooksd <- cooks.distance(Model02)
influential <- cooksd[(cooksd > (3 * mean(cooksd, na.rm = TRUE)))]
influential=as.numeric(names(influential))
z=z[-influential]
xaux=x[-influential]

Model02<-glm((z)~xaux,family=Gamma(link=log))
summary(Model02)
## 
## Call:
## glm(formula = (z) ~ xaux, family = Gamma(link = log))
## 
## Deviance Residuals: 
##    Min      1Q  Median      3Q     Max  
## -2.967  -1.859  -0.904   0.391   2.588  
## 
## Coefficients:
##                          Estimate Std. Error t value Pr(>|t|)    
## (Intercept)                5.4938     0.1618  33.956   <2e-16 ***
## xauxafter state of alarm   0.1221     0.2317   0.527    0.599    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for Gamma family taken to be 2.146411)
## 
##     Null deviance: 414.45  on 159  degrees of freedom
## Residual deviance: 413.85  on 158  degrees of freedom
## AIC: 2036.6
## 
## Number of Fisher Scoring iterations: 8

3.4 Dicologlossa cuneata

data1=subset(data_M,data_M$specie=="CET-ACEDIA")

Is gamma a correct distribution for the response variable PRICE?

summary(data1$euros)
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##   0.000   5.126   7.779   7.867   9.991  35.000
a=fitdist(data1$euros, distr = "gamma",start=list(shape = 1, rate = 2), lower = -1,method = "mme")
plot(a)

ks.test(na.omit(data1$euros), "pgamma", a$estimate[1],a$estimate[2])
## 
##  One-sample Kolmogorov-Smirnov test
## 
## data:  na.omit(data1$euros)
## D = 0.14111, p-value = 1.403e-05
## alternative hypothesis: two-sided
gamma_test(data1$euros)
## 
##  Test of fit for the Gamma distribution
## 
## data:  data1$euros
## V = NaN, p-value = NA

Is gamma a correct distribution for the response variable ABUNDANCE?

summary(data1$kgs)
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##   0.130   1.440   2.790   5.040   6.615  46.360
a=fitdist(data1$kgs, distr = "gamma",start=list(shape = 1, rate = 2), lower = -1,method = "mme")
plot(a)

ks.test(na.omit(data1$kgs), "pgamma", a$estimate[1],a$estimate[2])
## 
##  One-sample Kolmogorov-Smirnov test
## 
## data:  na.omit(data1$kgs)
## D = 0.13862, p-value = 2.124e-05
## alternative hypothesis: two-sided
gamma_test(data1$kgs)
## 
##  Test of fit for the Gamma distribution
## 
## data:  data1$kgs
## V = 6.0758, p-value = 1.737e-05

Regression models

ind=which(data1$euros==0)
if(length(ind)>0){data1=data1[-ind,]}


x=as.factor(data1$Covid)
levels(x)
## [1] "after state of alarm"           "Reference after state of alarm"
## [3] "Reference No"                   "Reference state of alarm 1"    
## [5] "Reference state of alarm 2"     "state of alarm 1"              
## [7] "state of alarm 2"
data0=subset(data1,data1$Covid=="Reference state of alarm 1" | data1$Covid=="state of alarm 1")
boxplot(data0$euros~data0$Covid, col = rgb(0, 0.5, 1, alpha = 0.5),xlab="Covid Category",ylab="Price")

boxplot(data0$kgs~data0$Covid, col = rgb(0, 0.5, 1, alpha = 0.5),xlab="Covid Category",ylab="Abundance")

x=as.factor(data0$Covid)
table(x)
## x
## Reference state of alarm 1           state of alarm 1 
##                         51                         39
y=as.numeric(data0$euros)
x=relevel(x, ref = "Reference state of alarm 1")
z=as.numeric(data0$kgs)

Model0<-glm((y)~x,family=Gamma(link=log))
cooksd <- cooks.distance(Model0)
influential <- cooksd[(cooksd > (3 * mean(cooksd, na.rm = TRUE)))]
influential=as.numeric(names(influential))
y=y[-influential]
xaux=x[-influential]
Model0<-glm((y)~xaux,family=Gamma(link=log))
summary(Model0)
## 
## Call:
## glm(formula = (y) ~ xaux, family = Gamma(link = log))
## 
## Deviance Residuals: 
##      Min        1Q    Median        3Q       Max  
## -1.12761  -0.16975   0.02368   0.22356   0.63240  
## 
## Coefficients:
##                      Estimate Std. Error t value Pr(>|t|)    
## (Intercept)           1.74033    0.05100  34.127  < 2e-16 ***
## xauxstate of alarm 1  0.28485    0.07357   3.872 0.000229 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for Gamma family taken to be 0.1040241)
## 
##     Null deviance: 12.012  on 76  degrees of freedom
## Residual deviance: 10.452  on 75  degrees of freedom
## AIC: 351.12
## 
## Number of Fisher Scoring iterations: 4
Model0<-glm((z)~x,family=Gamma(link=log))
summary(Model0)
## 
## Call:
## glm(formula = (z) ~ x, family = Gamma(link = log))
## 
## Deviance Residuals: 
##    Min      1Q  Median      3Q     Max  
## -1.719  -1.008  -0.363   0.279   2.564  
## 
## Coefficients:
##                   Estimate Std. Error t value Pr(>|t|)    
## (Intercept)         2.0292     0.1440  14.087   <2e-16 ***
## xstate of alarm 1  -0.1187     0.2188  -0.542    0.589    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for Gamma family taken to be 1.058176)
## 
##     Null deviance: 78.502  on 89  degrees of freedom
## Residual deviance: 78.193  on 88  degrees of freedom
## AIC: 539.31
## 
## Number of Fisher Scoring iterations: 5
data01=subset(data1,data1$Covid=="Reference state of alarm 2" | data1$Covid=="state of alarm 2")
boxplot(data01$euros~data01$Covid, col = rgb(0, 0.5, 1, alpha = 0.5),xlab="Covid Category",ylab="Price")

boxplot(data01$kgs~data01$Covid, col = rgb(0, 0.5, 1, alpha = 0.5),xlab="Covid Category",ylab="Abundance")

x=as.factor(data01$Covid)
table(x)
## x
## Reference state of alarm 2           state of alarm 2 
##                         38                         61
y=as.numeric(data01$euros)
x=relevel(x, ref = "Reference state of alarm 2")
z=as.numeric(data01$kgs)


Model01<-glm((y)~x,family=Gamma(link=log))
cooksd <- cooks.distance(Model01)
influential <- cooksd[(cooksd > (3 * mean(cooksd, na.rm = TRUE)))]
influential=as.numeric(names(influential))
y=y[-influential]
xaux=x[-influential]
Model01<-glm((y)~xaux,family=Gamma(link=log))
summary(Model01)
## 
## Call:
## glm(formula = (y) ~ xaux, family = Gamma(link = log))
## 
## Deviance Residuals: 
##      Min        1Q    Median        3Q       Max  
## -2.03463  -0.31421  -0.03373   0.32821   0.81116  
## 
## Coefficients:
##                      Estimate Std. Error t value Pr(>|t|)    
## (Intercept)           1.84755    0.07644  24.170  < 2e-16 ***
## xauxstate of alarm 2  0.44449    0.09545   4.657  1.1e-05 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for Gamma family taken to be 0.1928202)
## 
##     Null deviance: 28.814  on 91  degrees of freedom
## Residual deviance: 24.831  on 90  degrees of freedom
## AIC: 518.29
## 
## Number of Fisher Scoring iterations: 5
Model01<-glm((z)~x,family=Gamma(link=log))
cooksd <- cooks.distance(Model01)
influential <- cooksd[(cooksd > (3 * mean(cooksd, na.rm = TRUE)))]
influential=as.numeric(names(influential))
z=z[-influential]
xaux=x[-influential]

Model01<-glm((z)~xaux,family=Gamma(link=log))
summary(Model01)
## 
## Call:
## glm(formula = (z) ~ xaux, family = Gamma(link = log))
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -2.0694  -0.7748  -0.4105   0.4562   1.6553  
## 
## Coefficients:
##                      Estimate Std. Error t value Pr(>|t|)    
## (Intercept)            1.5570     0.1443   10.79  < 2e-16 ***
## xauxstate of alarm 2  -0.5011     0.1843   -2.72  0.00783 ** 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for Gamma family taken to be 0.7491782)
## 
##     Null deviance: 78.038  on 92  degrees of freedom
## Residual deviance: 72.341  on 91  degrees of freedom
## AIC: 418.53
## 
## Number of Fisher Scoring iterations: 5
data02=subset(data1,data1$Covid=="Reference after state of alarm" | data1$Covid=="after state of alarm")
boxplot(data02$euros~data02$Covid, col = rgb(0, 0.5, 1, alpha = 0.5),xlab="Covid Category",ylab="Price")

boxplot(data02$kgs~data02$Covid, col = rgb(0, 0.5, 1, alpha = 0.5),xlab="Covid Category",ylab="Abundance")

x=as.factor(data02$Covid)
table(x)
## x
##           after state of alarm Reference after state of alarm 
##                             32                             41
y=as.numeric(data02$euros)
x=relevel(x, ref = "Reference after state of alarm")
z=as.numeric(data02$kgs)

Model02<-glm((y)~x,family=Gamma(link=log))
cooksd <- cooks.distance(Model02)
influential <- cooksd[(cooksd > (3 * mean(cooksd, na.rm = TRUE)))]
influential=as.numeric(names(influential))
y=y[-influential]
xaux=x[-influential]
Model02<-glm((y)~xaux,family=Gamma(link=log))
summary(Model02)
## 
## Call:
## glm(formula = (y) ~ xaux, family = Gamma(link = log))
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -1.7713  -0.1808   0.0815   0.2281   0.7369  
## 
## Coefficients:
##                          Estimate Std. Error t value Pr(>|t|)    
## (Intercept)               2.12924    0.06406  33.238   <2e-16 ***
## xauxafter state of alarm -0.06022    0.09835  -0.612    0.543    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for Gamma family taken to be 0.15594)
## 
##     Null deviance: 18.118  on 65  degrees of freedom
## Residual deviance: 18.060  on 64  degrees of freedom
## AIC: 370.4
## 
## Number of Fisher Scoring iterations: 5
Model02<-glm((z)~x,family=Gamma(link=log))
cooksd <- cooks.distance(Model02)
influential <- cooksd[(cooksd > (3 * mean(cooksd, na.rm = TRUE)))]
influential=as.numeric(names(influential))
z=z[-influential]
xaux=x[-influential]

Model02<-glm((z)~xaux,family=Gamma(link=log))
summary(Model02)
## 
## Call:
## glm(formula = (z) ~ xaux, family = Gamma(link = log))
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -1.9880  -0.8074  -0.4233   0.3192   1.8220  
## 
## Coefficients:
##                          Estimate Std. Error t value Pr(>|t|)    
## (Intercept)                1.0251     0.1389   7.378 2.94e-10 ***
## xauxafter state of alarm  -0.2016     0.2122  -0.950    0.345    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for Gamma family taken to be 0.7720318)
## 
##     Null deviance: 51.490  on 69  degrees of freedom
## Residual deviance: 50.801  on 68  degrees of freedom
## AIC: 271.16
## 
## Number of Fisher Scoring iterations: 5

3.5 Parapenaeus longirostris

data1=subset(data_M,data_M$specie=="DPS-GAMBA")

Is gamma a correct distribution for the response variable PRICE?

summary(data1$euros)
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##   4.768  10.716  12.415  12.949  14.816  33.448
a=fitdist(data1$euros, distr = "gamma",start=list(shape = 1, rate = 2), lower = -1,method = "mme")
plot(a)

ks.test(na.omit(data1$euros), "pgamma", a$estimate[1],a$estimate[2])
## 
##  One-sample Kolmogorov-Smirnov test
## 
## data:  na.omit(data1$euros)
## D = 0.042294, p-value = 0.2301
## alternative hypothesis: two-sided
gamma_test(data1$euros)
## 
##  Test of fit for the Gamma distribution
## 
## data:  data1$euros
## V = 6.2093, p-value = 1.13e-05

Is gamma a correct distribution for the response variable ABUNDANCE?

summary(data1$kgs)
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##     2.9   933.2  1412.7  1449.3  2006.2  3258.3
a=fitdist(data1$kgs, distr = "gamma",start=list(shape = 1, rate = 2), lower = -1,method = "mme")
plot(a)

ks.test(na.omit(data1$kgs), "pgamma", a$estimate[1],a$estimate[2])
## 
##  One-sample Kolmogorov-Smirnov test
## 
## data:  na.omit(data1$kgs)
## D = 0.079542, p-value = 0.0009589
## alternative hypothesis: two-sided
gamma_test(data1$kgs)
## 
##  Test of fit for the Gamma distribution
## 
## data:  data1$kgs
## V = -11.372, p-value = 8.873e-16

Regression models

x=as.factor(data1$Covid)
levels(x)
## [1] "after state of alarm"           "Reference after state of alarm"
## [3] "Reference No"                   "Reference state of alarm 1"    
## [5] "Reference state of alarm 2"     "state of alarm 1"              
## [7] "state of alarm 2"
data0=subset(data1,data1$Covid=="Reference state of alarm 1" | data1$Covid=="state of alarm 1")
boxplot(data0$euros~data0$Covid, col = rgb(0, 0.5, 1, alpha = 0.5),xlab="Covid Category",ylab="Price")

boxplot(data0$kgs~data0$Covid, col = rgb(0, 0.5, 1, alpha = 0.5),xlab="Covid Category",ylab="Abundance")

x=as.factor(data0$Covid)
table(x)
## x
## Reference state of alarm 1           state of alarm 1 
##                         71                         70
y=as.numeric(data0$euros)
x=relevel(x, ref = "Reference state of alarm 1")
z=as.numeric(data0$kgs)

Model0<-glm((y)~x,family=Gamma(link=log))
cooksd <- cooks.distance(Model0)
influential <- cooksd[(cooksd > (3 * mean(cooksd, na.rm = TRUE)))]
influential=as.numeric(names(influential))
y=y[-influential]
xaux=x[-influential]
Model0<-glm((y)~xaux,family=Gamma(link=log))
summary(Model0)
## 
## Call:
## glm(formula = (y) ~ xaux, family = Gamma(link = log))
## 
## Deviance Residuals: 
##       Min         1Q     Median         3Q        Max  
## -0.313220  -0.105727  -0.005387   0.083521   0.310077  
## 
## Coefficients:
##                      Estimate Std. Error t value Pr(>|t|)    
## (Intercept)           2.70665    0.01636  165.48   <2e-16 ***
## xauxstate of alarm 1 -0.37839    0.02359  -16.04   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for Gamma family taken to be 0.01819258)
## 
##     Null deviance: 6.9899  on 130  degrees of freedom
## Residual deviance: 2.3575  on 129  degrees of freedom
## AIC: 510.96
## 
## Number of Fisher Scoring iterations: 4
Model0<-glm((z)~x,family=Gamma(link=log))
cooksd <- cooks.distance(Model0)
influential <- cooksd[(cooksd > (3 * mean(cooksd, na.rm = TRUE)))]
influential=as.numeric(names(influential))
z=z[-influential]
xaux=x[-influential]

Model0<-glm((z)~xaux,family=Gamma(link=log))
summary(Model0)
## 
## Call:
## glm(formula = (z) ~ xaux, family = Gamma(link = log))
## 
## Deviance Residuals: 
##      Min        1Q    Median        3Q       Max  
## -0.74769  -0.23178  -0.05673   0.16063   0.51690  
## 
## Coefficients:
##                      Estimate Std. Error t value Pr(>|t|)    
## (Intercept)           6.84779    0.03474 197.143  < 2e-16 ***
## xauxstate of alarm 1  0.39119    0.05073   7.711 3.28e-12 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for Gamma family taken to be 0.08204376)
## 
##     Null deviance: 15.416  on 127  degrees of freedom
## Residual deviance: 10.530  on 126  degrees of freedom
## AIC: 1840.7
## 
## Number of Fisher Scoring iterations: 4
data01=subset(data1,data1$Covid=="Reference state of alarm 2" | data1$Covid=="state of alarm 2")
boxplot(data01$euros~data01$Covid, col = rgb(0, 0.5, 1, alpha = 0.5),xlab="Covid Category",ylab="Price")

boxplot(data01$kgs~data01$Covid, col = rgb(0, 0.5, 1, alpha = 0.5),xlab="Covid Category",ylab="Abundance")

x=as.factor(data01$Covid)
table(x)
## x
## Reference state of alarm 2           state of alarm 2 
##                         98                        135
y=as.numeric(data01$euros)
x=relevel(x, ref = "Reference state of alarm 2")
z=as.numeric(data01$kgs)


Model01<-glm((y)~x,family=Gamma(link=log))
cooksd <- cooks.distance(Model01)
influential <- cooksd[(cooksd > (3 * mean(cooksd, na.rm = TRUE)))]
influential=as.numeric(names(influential))
y=y[-influential]
xaux=x[-influential]
Model01<-glm((y)~xaux,family=Gamma(link=log))
summary(Model01)
## 
## Call:
## glm(formula = (y) ~ xaux, family = Gamma(link = log))
## 
## Deviance Residuals: 
##      Min        1Q    Median        3Q       Max  
## -0.35057  -0.14441  -0.03856   0.07903   0.47156  
## 
## Coefficients:
##                      Estimate Std. Error t value Pr(>|t|)    
## (Intercept)           2.38991    0.01968 121.429  < 2e-16 ***
## xauxstate of alarm 2  0.18831    0.02578   7.305 4.98e-12 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for Gamma family taken to be 0.03602471)
## 
##     Null deviance: 9.2779  on 222  degrees of freedom
## Residual deviance: 7.3780  on 221  degrees of freedom
## AIC: 987.43
## 
## Number of Fisher Scoring iterations: 4
Model01<-glm((z)~x,family=Gamma(link=log))
cooksd <- cooks.distance(Model01)
influential <- cooksd[(cooksd > (3 * mean(cooksd, na.rm = TRUE)))]
influential=as.numeric(names(influential))
z=z[-influential]
xaux=x[-influential]

Model01<-glm((z)~xaux,family=Gamma(link=log))
summary(Model01)
## 
## Call:
## glm(formula = (z) ~ xaux, family = Gamma(link = log))
## 
## Deviance Residuals: 
##      Min        1Q    Median        3Q       Max  
## -1.25303  -0.27642   0.07417   0.24983   0.57416  
## 
## Coefficients:
##                      Estimate Std. Error t value Pr(>|t|)    
## (Intercept)           7.52108    0.03708 202.824  < 2e-16 ***
## xauxstate of alarm 2 -0.20739    0.04839  -4.286 2.75e-05 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for Gamma family taken to be 0.1237559)
## 
##     Null deviance: 40.457  on 217  degrees of freedom
## Residual deviance: 38.160  on 216  degrees of freedom
## AIC: 3439
## 
## Number of Fisher Scoring iterations: 4
data02=subset(data1,data1$Covid=="Reference after state of alarm" | data1$Covid=="after state of alarm")
boxplot(data02$euros~data02$Covid, col = rgb(0, 0.5, 1, alpha = 0.5),xlab="Covid Category",ylab="Price")

boxplot(data02$kgs~data02$Covid, col = rgb(0, 0.5, 1, alpha = 0.5),xlab="Covid Category",ylab="Abundance")

x=as.factor(data02$Covid)
table(x)
## x
##           after state of alarm Reference after state of alarm 
##                             89                             90
y=as.numeric(data02$euros)
x=relevel(x, ref = "Reference after state of alarm")
z=as.numeric(data02$kgs)

Model02<-glm((y)~x,family=Gamma(link=log))
cooksd <- cooks.distance(Model02)
influential <- cooksd[(cooksd > (3 * mean(cooksd, na.rm = TRUE)))]
influential=as.numeric(names(influential))
y=y[-influential]
xaux=x[-influential]
Model02<-glm((y)~xaux,family=Gamma(link=log))
summary(Model02)
## 
## Call:
## glm(formula = (y) ~ xaux, family = Gamma(link = log))
## 
## Deviance Residuals: 
##      Min        1Q    Median        3Q       Max  
## -0.35227  -0.13916  -0.02574   0.15329   0.30656  
## 
## Coefficients:
##                          Estimate Std. Error t value Pr(>|t|)    
## (Intercept)               2.66264    0.01921  138.60  < 2e-16 ***
## xauxafter state of alarm -0.08268    0.02694   -3.07  0.00249 ** 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for Gamma family taken to be 0.03137133)
## 
##     Null deviance: 5.7915  on 172  degrees of freedom
## Residual deviance: 5.4958  on 171  degrees of freedom
## AIC: 802.36
## 
## Number of Fisher Scoring iterations: 4
Model02<-glm((z)~x,family=Gamma(link=log))
cooksd <- cooks.distance(Model02)
influential <- cooksd[(cooksd > (3 * mean(cooksd, na.rm = TRUE)))]
influential=as.numeric(names(influential))
z=z[-influential]
xaux=x[-influential]

Model02<-glm((z)~xaux,family=Gamma(link=log))
summary(Model02)
## 
## Call:
## glm(formula = (z) ~ xaux, family = Gamma(link = log))
## 
## Deviance Residuals: 
##      Min        1Q    Median        3Q       Max  
## -0.73894  -0.24586  -0.02574   0.18830   0.65658  
## 
## Coefficients:
##                          Estimate Std. Error t value Pr(>|t|)    
## (Intercept)               6.99568    0.03471  201.52   <2e-16 ***
## xauxafter state of alarm  0.63519    0.04768   13.32   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for Gamma family taken to be 0.09399784)
## 
##     Null deviance: 31.373  on 165  degrees of freedom
## Residual deviance: 15.165  on 164  degrees of freedom
## AIC: 2501.6
## 
## Number of Fisher Scoring iterations: 4

3.6 Phycis phycis

data1=subset(data_M,data_M$specie=="FOR-BROTOLA DE ROCA")

Is gamma a correct distribution for the response variable PRICE?

summary(data1$euros)
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##   2.905   6.318   7.203   7.393   8.277  13.537
a=fitdist(data1$euros, distr = "gamma",start=list(shape = 1, rate = 2), lower = -1,method = "mme")
plot(a)

ks.test(na.omit(data1$euros), "pgamma", a$estimate[1],a$estimate[2])
## 
##  One-sample Kolmogorov-Smirnov test
## 
## data:  na.omit(data1$euros)
## D = 0.059268, p-value = 0.03305
## alternative hypothesis: two-sided
gamma_test(data1$euros)
## 
##  Test of fit for the Gamma distribution
## 
## data:  data1$euros
## V = -0.95407, p-value = 0.4999

Is gamma a correct distribution for the response variable ABUNDANCE?

summary(data1$kgs)
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##   0.280   6.897  17.460  31.183  39.595 326.690
a=fitdist(data1$kgs, distr = "gamma",start=list(shape = 1, rate = 2), lower = -1,method = "mme")
plot(a)

ks.test(na.omit(data1$kgs), "pgamma", a$estimate[1],a$estimate[2])
## 
##  One-sample Kolmogorov-Smirnov test
## 
## data:  na.omit(data1$kgs)
## D = 0.089832, p-value = 0.0001613
## alternative hypothesis: two-sided
gamma_test(data1$kgs)
## 
##  Test of fit for the Gamma distribution
## 
## data:  data1$kgs
## V = 6.6581, p-value = 2.502e-06

Regression models

x=as.factor(data1$Covid)
levels(x)
## [1] "after state of alarm"           "Reference after state of alarm"
## [3] "Reference No"                   "Reference state of alarm 1"    
## [5] "Reference state of alarm 2"     "state of alarm 1"              
## [7] "state of alarm 2"
data0=subset(data1,data1$Covid=="Reference state of alarm 1" | data1$Covid=="state of alarm 1")
boxplot(data0$euros~data0$Covid, col = rgb(0, 0.5, 1, alpha = 0.5),xlab="Covid Category",ylab="Price")

boxplot(data0$kgs~data0$Covid, col = rgb(0, 0.5, 1, alpha = 0.5),xlab="Covid Category",ylab="Abundance")

x=as.factor(data0$Covid)
table(x)
## x
## Reference state of alarm 1           state of alarm 1 
##                         66                         62
y=as.numeric(data0$euros)
x=relevel(x, ref = "Reference state of alarm 1")
z=as.numeric(data0$kgs)

Model0<-glm((y)~x,family=Gamma(link=log))
cooksd <- cooks.distance(Model0)
influential <- cooksd[(cooksd > (3 * mean(cooksd, na.rm = TRUE)))]
influential=as.numeric(names(influential))
y=y[-influential]
xaux=x[-influential]
Model0<-glm((y)~xaux,family=Gamma(link=log))
summary(Model0)
## 
## Call:
## glm(formula = (y) ~ xaux, family = Gamma(link = log))
## 
## Deviance Residuals: 
##      Min        1Q    Median        3Q       Max  
## -0.45910  -0.17091   0.00604   0.14402   0.34529  
## 
## Coefficients:
##                      Estimate Std. Error t value Pr(>|t|)    
## (Intercept)           1.89911    0.02366  80.263  < 2e-16 ***
## xauxstate of alarm 1  0.14403    0.03451   4.174 5.85e-05 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for Gamma family taken to be 0.03471049)
## 
##     Null deviance: 4.7233  on 116  degrees of freedom
## Residual deviance: 4.1175  on 115  degrees of freedom
## AIC: 403.24
## 
## Number of Fisher Scoring iterations: 4
Model0<-glm((z)~x,family=Gamma(link=log))
cooksd <- cooks.distance(Model0)
influential <- cooksd[(cooksd > (3 * mean(cooksd, na.rm = TRUE)))]
influential=as.numeric(names(influential))
z=z[-influential]
xaux=x[-influential]

Model0<-glm((z)~xaux,family=Gamma(link=log))
summary(Model0)
## 
## Call:
## glm(formula = (z) ~ xaux, family = Gamma(link = log))
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -2.3838  -0.8398  -0.2796   0.3314   1.3761  
## 
## Coefficients:
##                      Estimate Std. Error t value Pr(>|t|)    
## (Intercept)           3.31266    0.09928  33.368  < 2e-16 ***
## xauxstate of alarm 1 -0.76620    0.14464  -5.297 5.47e-07 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for Gamma family taken to be 0.6307705)
## 
##     Null deviance: 123.60  on 120  degrees of freedom
## Residual deviance: 106.56  on 119  degrees of freedom
## AIC: 959.13
## 
## Number of Fisher Scoring iterations: 5
data01=subset(data1,data1$Covid=="Reference state of alarm 2" | data1$Covid=="state of alarm 2")
boxplot(data01$euros~data01$Covid, col = rgb(0, 0.5, 1, alpha = 0.5),xlab="Covid Category",ylab="Price")

boxplot(data01$kgs~data01$Covid, col = rgb(0, 0.5, 1, alpha = 0.5),xlab="Covid Category",ylab="Abundance")

x=as.factor(data01$Covid)
table(x)
## x
## Reference state of alarm 2           state of alarm 2 
##                         98                        121
y=as.numeric(data01$euros)
x=relevel(x, ref = "Reference state of alarm 2")
z=as.numeric(data01$kgs)


Model01<-glm((y)~x,family=Gamma(link=log))
cooksd <- cooks.distance(Model01)
influential <- cooksd[(cooksd > (3 * mean(cooksd, na.rm = TRUE)))]
influential=as.numeric(names(influential))
y=y[-influential]
xaux=x[-influential]
Model01<-glm((y)~xaux,family=Gamma(link=log))
summary(Model01)
## 
## Call:
## glm(formula = (y) ~ xaux, family = Gamma(link = log))
## 
## Deviance Residuals: 
##      Min        1Q    Median        3Q       Max  
## -0.33701  -0.13816  -0.02442   0.11082   0.32036  
## 
## Coefficients:
##                      Estimate Std. Error t value Pr(>|t|)    
## (Intercept)           1.97081    0.01655 119.113   <2e-16 ***
## xauxstate of alarm 2  0.03800    0.02264   1.679   0.0948 .  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for Gamma family taken to be 0.02600729)
## 
##     Null deviance: 5.2628  on 203  degrees of freedom
## Residual deviance: 5.1896  on 202  degrees of freedom
## AIC: 644
## 
## Number of Fisher Scoring iterations: 4
Model01<-glm((z)~x,family=Gamma(link=log))
cooksd <- cooks.distance(Model01)
influential <- cooksd[(cooksd > (3 * mean(cooksd, na.rm = TRUE)))]
influential=as.numeric(names(influential))
z=z[-influential]
xaux=x[-influential]

Model01<-glm((z)~xaux,family=Gamma(link=log))
summary(Model01)
## 
## Call:
## glm(formula = (z) ~ xaux, family = Gamma(link = log))
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -2.6628  -1.2295  -0.5782   0.4450   1.9468  
## 
## Coefficients:
##                      Estimate Std. Error t value Pr(>|t|)    
## (Intercept)            2.8528     0.1101   25.91   <2e-16 ***
## xauxstate of alarm 2   0.4086     0.1486    2.75   0.0065 ** 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for Gamma family taken to be 1.115233)
## 
##     Null deviance: 273.18  on 203  degrees of freedom
## Residual deviance: 264.91  on 202  degrees of freedom
## AIC: 1671.1
## 
## Number of Fisher Scoring iterations: 6
data02=subset(data1,data1$Covid=="Reference after state of alarm" | data1$Covid=="after state of alarm")
boxplot(data02$euros~data02$Covid, col = rgb(0, 0.5, 1, alpha = 0.5),xlab="Covid Category",ylab="Price")

boxplot(data02$kgs~data02$Covid, col = rgb(0, 0.5, 1, alpha = 0.5),xlab="Covid Category",ylab="Abundance")

x=as.factor(data02$Covid)
table(x)
## x
##           after state of alarm Reference after state of alarm 
##                             93                             94
y=as.numeric(data02$euros)
x=relevel(x, ref = "Reference after state of alarm")
z=as.numeric(data02$kgs)

Model02<-glm((y)~x,family=Gamma(link=log))
cooksd <- cooks.distance(Model02)
influential <- cooksd[(cooksd > (3 * mean(cooksd, na.rm = TRUE)))]
influential=as.numeric(names(influential))
y=y[-influential]
xaux=x[-influential]
Model02<-glm((y)~xaux,family=Gamma(link=log))
summary(Model02)
## 
## Call:
## glm(formula = (y) ~ xaux, family = Gamma(link = log))
## 
## Deviance Residuals: 
##      Min        1Q    Median        3Q       Max  
## -0.38714  -0.12665  -0.01183   0.10244   0.30856  
## 
## Coefficients:
##                          Estimate Std. Error t value Pr(>|t|)    
## (Intercept)               1.97311    0.01675 117.774  < 2e-16 ***
## xauxafter state of alarm  0.07214    0.02376   3.036  0.00278 ** 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for Gamma family taken to be 0.02385735)
## 
##     Null deviance: 4.2046  on 168  degrees of freedom
## Residual deviance: 3.9847  on 167  degrees of freedom
## AIC: 528
## 
## Number of Fisher Scoring iterations: 4
Model02<-glm((z)~x,family=Gamma(link=log))
cooksd <- cooks.distance(Model02)
influential <- cooksd[(cooksd > (3 * mean(cooksd, na.rm = TRUE)))]
influential=as.numeric(names(influential))
z=z[-influential]
xaux=x[-influential]

Model02<-glm((z)~xaux,family=Gamma(link=log))
summary(Model02)
## 
## Call:
## glm(formula = (z) ~ xaux, family = Gamma(link = log))
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -2.3390  -0.9348  -0.1996   0.3198   1.7016  
## 
## Coefficients:
##                          Estimate Std. Error t value Pr(>|t|)    
## (Intercept)               3.48792    0.09042  38.574   <2e-16 ***
## xauxafter state of alarm -0.10718    0.12935  -0.829    0.408    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for Gamma family taken to be 0.7358343)
## 
##     Null deviance: 153.86  on 175  degrees of freedom
## Residual deviance: 153.35  on 174  degrees of freedom
## AIC: 1562.2
## 
## Number of Fisher Scoring iterations: 5

3.7 Auxis thazard

data1=subset(data_M,data_M$specie=="FRZ-MELVAS")

Is gamma a correct distribution for the response variable PRICE?

summary(data1$euros)
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##   0.600   1.260   1.667   2.322   3.079  33.397
a=fitdist(data1$euros, distr = "gamma",start=list(shape = 1, rate = 2), lower = -1,method = "mme")
plot(a)

ks.test(na.omit(data1$euros), "pgamma", a$estimate[1],a$estimate[2])
## 
##  One-sample Kolmogorov-Smirnov test
## 
## data:  na.omit(data1$euros)
## D = 0.39535, p-value < 2.2e-16
## alternative hypothesis: two-sided
gamma_test(data1$euros)
## 
##  Test of fit for the Gamma distribution
## 
## data:  data1$euros
## V = 35.245, p-value < 2.2e-16

Is gamma a correct distribution for the response variable ABUNDANCE?

summary(data1$kgs)
##     Min.  1st Qu.   Median     Mean  3rd Qu.     Max. 
##     1.00     4.74   135.00  4602.30  4511.52 71910.08
a=fitdist(data1$kgs, distr = "gamma",start=list(shape = 1, rate = 2), lower = -1,method = "mme")
plot(a)

ks.test(na.omit(data1$kgs), "pgamma", a$estimate[1],a$estimate[2])
## 
##  One-sample Kolmogorov-Smirnov test
## 
## data:  na.omit(data1$kgs)
## D = 0.21121, p-value = 9.666e-07
## alternative hypothesis: two-sided
gamma_test(data1$kgs)
## 
##  Test of fit for the Gamma distribution
## 
## data:  data1$kgs
## V = -0.67555, p-value = 0.6329

Regression models

x=as.factor(data1$Covid)
levels(x)
## [1] "after state of alarm"           "Reference after state of alarm"
## [3] "Reference No"                   "Reference state of alarm 1"    
## [5] "Reference state of alarm 2"     "state of alarm 1"              
## [7] "state of alarm 2"
data0=subset(data1,data1$Covid=="Reference state of alarm 1" | data1$Covid=="state of alarm 1")
boxplot(data0$euros~data0$Covid, col = rgb(0, 0.5, 1, alpha = 0.5),xlab="Covid Category",ylab="Price")

boxplot(data0$kgs~data0$Covid, col = rgb(0, 0.5, 1, alpha = 0.5),xlab="Covid Category",ylab="Abundance")

x=as.factor(data0$Covid)
table(x)
## x
## Reference state of alarm 1           state of alarm 1 
##                         10                          8
y=as.numeric(data0$euros)
x=relevel(x, ref = "Reference state of alarm 1")
z=as.numeric(data0$kgs)

Model0<-glm((y)~x,family=Gamma(link=log))
cooksd <- cooks.distance(Model0)
influential <- cooksd[(cooksd > (3 * mean(cooksd, na.rm = TRUE)))]
influential=as.numeric(names(influential))
y=y[-influential]
xaux=x[-influential]
Model0<-glm((y)~xaux,family=Gamma(link=log))
summary(Model0)
## 
## Call:
## glm(formula = (y) ~ xaux, family = Gamma(link = log))
## 
## Deviance Residuals: 
##      Min        1Q    Median        3Q       Max  
## -0.49496  -0.43183   0.00534   0.27505   0.48762  
## 
## Coefficients:
##                      Estimate Std. Error t value Pr(>|t|)    
## (Intercept)            0.8018     0.1139   7.040    4e-06 ***
## xauxstate of alarm 1   0.3057     0.1660   1.842   0.0854 .  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for Gamma family taken to be 0.1167286)
## 
##     Null deviance: 2.2656  on 16  degrees of freedom
## Residual deviance: 1.8689  on 15  degrees of freedom
## AIC: 47.304
## 
## Number of Fisher Scoring iterations: 4
Model0<-glm((z)~x,family=Gamma(link=log))
cooksd <- cooks.distance(Model0)
influential <- cooksd[(cooksd > (3 * mean(cooksd, na.rm = TRUE)))]
influential=as.numeric(names(influential))
z=z[-influential]
xaux=x[-influential]

Model0<-glm((z)~xaux,family=Gamma(link=log))
summary(Model0)
## 
## Call:
## glm(formula = (z) ~ xaux, family = Gamma(link = log))
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -1.0637  -0.9070  -0.2785   0.1228   1.2963  
## 
## Coefficients:
##                      Estimate Std. Error t value Pr(>|t|)    
## (Intercept)            1.2906     0.2644   4.882 0.000199 ***
## xauxstate of alarm 1   0.5275     0.4120   1.280 0.219871    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for Gamma family taken to be 0.6988602)
## 
##     Null deviance: 10.9617  on 16  degrees of freedom
## Residual deviance:  9.7933  on 15  degrees of freedom
## AIC: 87.956
## 
## Number of Fisher Scoring iterations: 5
data01=subset(data1,data1$Covid=="Reference state of alarm 2" | data1$Covid=="state of alarm 2")
boxplot(data01$euros~data01$Covid, col = rgb(0, 0.5, 1, alpha = 0.5),xlab="Covid Category",ylab="Price")

boxplot(data01$kgs~data01$Covid, col = rgb(0, 0.5, 1, alpha = 0.5),xlab="Covid Category",ylab="Abundance")

x=as.factor(data01$Covid)
table(x)
## x
## Reference state of alarm 2           state of alarm 2 
##                         26                         19
y=as.numeric(data01$euros)
x=relevel(x, ref = "Reference state of alarm 2")
z=as.numeric(data01$kgs)


Model01<-glm((y)~x,family=Gamma(link=log))
cooksd <- cooks.distance(Model01)
influential <- cooksd[(cooksd > (3 * mean(cooksd, na.rm = TRUE)))]
influential=as.numeric(names(influential))
y=y[-influential]
xaux=x[-influential]
Model01<-glm((y)~xaux,family=Gamma(link=log))
summary(Model01)
## 
## Call:
## glm(formula = (y) ~ xaux, family = Gamma(link = log))
## 
## Deviance Residuals: 
##      Min        1Q    Median        3Q       Max  
## -1.07672  -0.50650  -0.08795   0.37995   0.68138  
## 
## Coefficients:
##                      Estimate Std. Error t value Pr(>|t|)    
## (Intercept)           0.79896    0.09067   8.812 4.25e-11 ***
## xauxstate of alarm 2 -0.02197    0.13798  -0.159    0.874    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for Gamma family taken to be 0.2055157)
## 
##     Null deviance: 9.2579  on 43  degrees of freedom
## Residual deviance: 9.2527  on 42  degrees of freedom
## AIC: 124.02
## 
## Number of Fisher Scoring iterations: 4
Model01<-glm((z)~x,family=Gamma(link=log))
## Warning: glm.fit: algorithm did not converge
cooksd <- cooks.distance(Model01)
influential <- cooksd[(cooksd > (3 * mean(cooksd, na.rm = TRUE)))]
influential=as.numeric(names(influential))
z=z[-influential]
xaux=x[-influential]

Model01<-glm((z)~xaux,family=Gamma(link=log))
## Warning: glm.fit: algorithm did not converge
summary(Model01)
## 
## Call:
## glm(formula = (z) ~ xaux, family = Gamma(link = log))
## 
## Deviance Residuals: 
##    Min      1Q  Median      3Q     Max  
## -7.720  -7.321  -6.487  -2.835   2.901  
## 
## Coefficients:
##                      Estimate Std. Error t value Pr(>|t|)    
## (Intercept)           30.8925     0.2906   106.3   <2e-16 ***
## xauxstate of alarm 2 -23.9681     0.4709   -50.9   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for Gamma family taken to be 2.196341)
## 
##     Null deviance:  318.35  on 41  degrees of freedom
## Residual deviance: 1427.73  on 40  degrees of freedom
## AIC: 702.28
## 
## Number of Fisher Scoring iterations: 25
data02=subset(data1,data1$Covid=="Reference after state of alarm" | data1$Covid=="after state of alarm")
boxplot(data02$euros~data02$Covid, col = rgb(0, 0.5, 1, alpha = 0.5),xlab="Covid Category",ylab="Price")

boxplot(data02$kgs~data02$Covid, col = rgb(0, 0.5, 1, alpha = 0.5),xlab="Covid Category",ylab="Abundance")

x=as.factor(data02$Covid)
table(x)
## x
##           after state of alarm Reference after state of alarm 
##                             36                             52
y=as.numeric(data02$euros)
x=relevel(x, ref = "Reference after state of alarm")
z=as.numeric(data02$kgs)

Model02<-glm((y)~x,family=Gamma(link=log))
cooksd <- cooks.distance(Model02)
influential <- cooksd[(cooksd > (3 * mean(cooksd, na.rm = TRUE)))]
influential=as.numeric(names(influential))
y=y[-influential]
xaux=x[-influential]
Model02<-glm((y)~xaux,family=Gamma(link=log))
summary(Model02)
## 
## Call:
## glm(formula = (y) ~ xaux, family = Gamma(link = log))
## 
## Deviance Residuals: 
##      Min        1Q    Median        3Q       Max  
## -0.85162  -0.42242  -0.17123   0.03374   1.53919  
## 
## Coefficients:
##                          Estimate Std. Error t value Pr(>|t|)    
## (Intercept)               0.54503    0.07573   7.197 2.27e-10 ***
## xauxafter state of alarm  0.11480    0.11940   0.962    0.339    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for Gamma family taken to be 0.2982383)
## 
##     Null deviance: 19.613  on 86  degrees of freedom
## Residual deviance: 19.336  on 85  degrees of freedom
## AIC: 208.8
## 
## Number of Fisher Scoring iterations: 5
Model02<-glm((z)~x,family=Gamma(link=log))
cooksd <- cooks.distance(Model02)
influential <- cooksd[(cooksd > (3 * mean(cooksd, na.rm = TRUE)))]
influential=as.numeric(names(influential))
z=z[-influential]
xaux=x[-influential]

Model02<-glm((z)~xaux,family=Gamma(link=log))
summary(Model02)
## 
## Call:
## glm(formula = (z) ~ xaux, family = Gamma(link = log))
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -3.8334  -2.3107  -0.6497   0.5261   2.0048  
## 
## Coefficients:
##                          Estimate Std. Error t value Pr(>|t|)    
## (Intercept)                9.0650     0.1782  50.872  < 2e-16 ***
## xauxafter state of alarm  -1.6913     0.2843  -5.949 6.37e-08 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for Gamma family taken to be 1.619343)
## 
##     Null deviance: 360.71  on 83  degrees of freedom
## Residual deviance: 313.99  on 82  degrees of freedom
## AIC: 1495.9
## 
## Number of Fisher Scoring iterations: 10

3.8 Euthynnus alletteratus

data1=subset(data_M,data_M$specie=="LTA-BACORETA")

Is gamma a correct distribution for the response variable PRICE?

summary(data1$euros)
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##  0.2349  1.5590  2.5768  2.5806  3.4565  6.9005
a=fitdist(data1$euros, distr = "gamma",start=list(shape = 1, rate = 2), lower = -1,method = "mme")
plot(a)

ks.test(na.omit(data1$euros), "pgamma", a$estimate[1],a$estimate[2])
## 
##  One-sample Kolmogorov-Smirnov test
## 
## data:  na.omit(data1$euros)
## D = 0.080799, p-value = 0.01916
## alternative hypothesis: two-sided
gamma_test(data1$euros)
## 
##  Test of fit for the Gamma distribution
## 
## data:  data1$euros
## V = -4.3852, p-value = 0.00193

Is gamma a correct distribution for the response variable ABUNDANCE?

summary(data1$kgs)
##     Min.  1st Qu.   Median     Mean  3rd Qu.     Max. 
##     0.80    12.24    36.05   742.82   174.39 13036.82
a=fitdist(data1$kgs, distr = "gamma",start=list(shape = 1, rate = 2), lower = -1,method = "mme")
plot(a)

ks.test(na.omit(data1$kgs), "pgamma", a$estimate[1],a$estimate[2])
## 
##  One-sample Kolmogorov-Smirnov test
## 
## data:  na.omit(data1$kgs)
## D = 0.36571, p-value < 2.2e-16
## alternative hypothesis: two-sided
gamma_test(data1$kgs)
## 
##  Test of fit for the Gamma distribution
## 
## data:  data1$kgs
## V = 6.958, p-value = 8.652e-07

Regression models

x=as.factor(data1$Covid)
levels(x)
## [1] "after state of alarm"           "Reference after state of alarm"
## [3] "Reference No"                   "Reference state of alarm 1"    
## [5] "Reference state of alarm 2"     "state of alarm 1"              
## [7] "state of alarm 2"
data0=subset(data1,data1$Covid=="Reference state of alarm 1" | data1$Covid=="state of alarm 1")
boxplot(data0$euros~data0$Covid, col = rgb(0, 0.5, 1, alpha = 0.5),xlab="Covid Category",ylab="Price")

boxplot(data0$kgs~data0$Covid, col = rgb(0, 0.5, 1, alpha = 0.5),xlab="Covid Category",ylab="Abundance")

x=as.factor(data0$Covid)
table(x)
## x
## Reference state of alarm 1           state of alarm 1 
##                         33                         35
y=as.numeric(data0$euros)
x=relevel(x, ref = "Reference state of alarm 1")
z=as.numeric(data0$kgs)

Model0<-glm((y)~x,family=Gamma(link=log))
cooksd <- cooks.distance(Model0)
influential <- cooksd[(cooksd > (3 * mean(cooksd, na.rm = TRUE)))]
influential=as.numeric(names(influential))
y=y[-influential]
xaux=x[-influential]
Model0<-glm((y)~xaux,family=Gamma(link=log))
summary(Model0)
## 
## Call:
## glm(formula = (y) ~ xaux, family = Gamma(link = log))
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -0.6834  -0.1466   0.0310   0.1779   0.5030  
## 
## Coefficients:
##                      Estimate Std. Error t value Pr(>|t|)    
## (Intercept)           1.21005    0.04789  25.268  < 2e-16 ***
## xauxstate of alarm 1 -0.18772    0.06666  -2.816  0.00657 ** 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for Gamma family taken to be 0.06880033)
## 
##     Null deviance: 5.2346  on 61  degrees of freedom
## Residual deviance: 4.6887  on 60  degrees of freedom
## AIC: 155.99
## 
## Number of Fisher Scoring iterations: 4
Model0<-glm((z)~x,family=Gamma(link=log))

cooksd <- cooks.distance(Model0)
influential <- cooksd[(cooksd > (3 * mean(cooksd, na.rm = TRUE)))]
influential=as.numeric(names(influential))
z=z[-influential]
xaux=x[-influential]

Model0<-glm((z)~xaux,family=Gamma(link=log))
summary(Model0)
## 
## Call:
## glm(formula = (z) ~ xaux, family = Gamma(link = log))
## 
## Deviance Residuals: 
##      Min        1Q    Median        3Q       Max  
## -2.58209  -1.46039  -0.81393   0.08539   2.83794  
## 
## Coefficients:
##                      Estimate Std. Error t value Pr(>|t|)    
## (Intercept)            4.2045     0.2634  15.963   <2e-16 ***
## xauxstate of alarm 1   0.1158     0.3696   0.313    0.755    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for Gamma family taken to be 2.15071)
## 
##     Null deviance: 114.91  on 62  degrees of freedom
## Residual deviance: 114.70  on 61  degrees of freedom
## AIC: 662.8
## 
## Number of Fisher Scoring iterations: 6
data01=subset(data1,data1$Covid=="Reference state of alarm 2" | data1$Covid=="state of alarm 2")
boxplot(data01$euros~data01$Covid, col = rgb(0, 0.5, 1, alpha = 0.5),xlab="Covid Category",ylab="Price")

boxplot(data01$kgs~data01$Covid, col = rgb(0, 0.5, 1, alpha = 0.5),xlab="Covid Category",ylab="Abundance")

x=as.factor(data01$Covid)
table(x)
## x
## Reference state of alarm 2           state of alarm 2 
##                         68                         86
y=as.numeric(data01$euros)
x=relevel(x, ref = "Reference state of alarm 2")
z=as.numeric(data01$kgs)


Model01<-glm((y)~x,family=Gamma(link=log))
cooksd <- cooks.distance(Model01)
influential <- cooksd[(cooksd > (3 * mean(cooksd, na.rm = TRUE)))]
influential=as.numeric(names(influential))
y=y[-influential]
xaux=x[-influential]
Model01<-glm((y)~xaux,family=Gamma(link=log))
summary(Model01)
## 
## Call:
## glm(formula = (y) ~ xaux, family = Gamma(link = log))
## 
## Deviance Residuals: 
##      Min        1Q    Median        3Q       Max  
## -1.27492  -0.58443   0.05088   0.32915   0.70540  
## 
## Coefficients:
##                      Estimate Std. Error t value Pr(>|t|)    
## (Intercept)           0.86420    0.05866  14.731   <2e-16 ***
## xauxstate of alarm 2  0.05617    0.07819   0.718    0.474    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for Gamma family taken to be 0.2271393)
## 
##     Null deviance: 43.710  on 150  degrees of freedom
## Residual deviance: 43.593  on 149  degrees of freedom
## AIC: 481.11
## 
## Number of Fisher Scoring iterations: 5
Model01<-glm((z)~x,family=Gamma(link=log))
cooksd <- cooks.distance(Model01)
influential <- cooksd[(cooksd > (3 * mean(cooksd, na.rm = TRUE)))]
influential=as.numeric(names(influential))
z=z[-influential]
xaux=x[-influential]

Model01<-glm((z)~xaux,family=Gamma(link=log))
summary(Model01)
## 
## Call:
## glm(formula = (z) ~ xaux, family = Gamma(link = log))
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -3.0774  -2.3234  -1.5912  -0.5544   5.5484  
## 
## Coefficients:
##                      Estimate Std. Error t value Pr(>|t|)    
## (Intercept)            6.7793     0.3112  21.782  < 2e-16 ***
## xauxstate of alarm 2  -1.7285     0.4179  -4.137 5.97e-05 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for Gamma family taken to be 6.296464)
## 
##     Null deviance: 670.20  on 145  degrees of freedom
## Residual deviance: 568.12  on 144  degrees of freedom
## AIC: 1828
## 
## Number of Fisher Scoring iterations: 14
data02=subset(data1,data1$Covid=="Reference after state of alarm" | data1$Covid=="after state of alarm")
boxplot(data02$euros~data02$Covid, col = rgb(0, 0.5, 1, alpha = 0.5),xlab="Covid Category",ylab="Price")

boxplot(data02$kgs~data02$Covid, col = rgb(0, 0.5, 1, alpha = 0.5),xlab="Covid Category",ylab="Abundance")

x=as.factor(data02$Covid)
table(x)
## x
##           after state of alarm Reference after state of alarm 
##                             56                             48
y=as.numeric(data02$euros)
x=relevel(x, ref = "Reference after state of alarm")
z=as.numeric(data02$kgs)

Model02<-glm((y)~x,family=Gamma(link=log))
cooksd <- cooks.distance(Model02)
influential <- cooksd[(cooksd > (3 * mean(cooksd, na.rm = TRUE)))]
influential=as.numeric(names(influential))
y=y[-influential]
xaux=x[-influential]
Model02<-glm((y)~xaux,family=Gamma(link=log))
summary(Model02)
## 
## Call:
## glm(formula = (y) ~ xaux, family = Gamma(link = log))
## 
## Deviance Residuals: 
##      Min        1Q    Median        3Q       Max  
## -1.54488  -0.52852   0.01512   0.30054   0.94947  
## 
## Coefficients:
##                          Estimate Std. Error t value Pr(>|t|)    
## (Intercept)               0.61815    0.07460   8.286 8.53e-13 ***
## xauxafter state of alarm  0.05456    0.10182   0.536    0.593    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for Gamma family taken to be 0.2448801)
## 
##     Null deviance: 27.068  on 94  degrees of freedom
## Residual deviance: 26.998  on 93  degrees of freedom
## AIC: 256.58
## 
## Number of Fisher Scoring iterations: 5
Model02<-glm((z)~x,family=Gamma(link=log))
cooksd <- cooks.distance(Model02)
influential <- cooksd[(cooksd > (3 * mean(cooksd, na.rm = TRUE)))]
influential=as.numeric(names(influential))
z=z[-influential]
xaux=x[-influential]

Model02<-glm((z)~xaux,family=Gamma(link=log))
summary(Model02)
## 
## Call:
## glm(formula = (z) ~ xaux, family = Gamma(link = log))
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -3.3745  -2.1701  -1.4925  -0.1393   3.6145  
## 
## Coefficients:
##                          Estimate Std. Error t value Pr(>|t|)    
## (Intercept)                5.2059     0.2865  18.174  < 2e-16 ***
## xauxafter state of alarm   1.7097     0.3859   4.431 2.49e-05 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for Gamma family taken to be 3.61052)
## 
##     Null deviance: 404.17  on 97  degrees of freedom
## Residual deviance: 343.58  on 96  degrees of freedom
## AIC: 1316.7
## 
## Number of Fisher Scoring iterations: 11

3.9 Nephrops norvegicus

data1=subset(data_M,data_M$specie=="NEP-CIGALA")

Is gamma a correct distribution for the response variable PRICE?

summary(data1$euros)
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##   21.84   32.10   36.85   38.01   41.82   84.20
a=fitdist(data1$euros, distr = "gamma",start=list(shape = 1, rate = 2), lower = -1,method = "mme")
plot(a)

ks.test(na.omit(data1$euros), "pgamma", a$estimate[1],a$estimate[2])
## 
##  One-sample Kolmogorov-Smirnov test
## 
## data:  na.omit(data1$euros)
## D = 0.076884, p-value = 0.001681
## alternative hypothesis: two-sided
gamma_test(data1$euros)
## 
##  Test of fit for the Gamma distribution
## 
## data:  data1$euros
## V = 10.133, p-value = 7.786e-13

Is gamma a correct distribution for the response variable ABUNDANCE?

summary(data1$kgs)
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##    0.30   51.89  105.90  133.76  194.17  563.56
a=fitdist(data1$kgs, distr = "gamma",start=list(shape = 1, rate = 2), lower = -1,method = "mme")
plot(a)

ks.test(na.omit(data1$kgs), "pgamma", a$estimate[1],a$estimate[2])
## 
##  One-sample Kolmogorov-Smirnov test
## 
## data:  na.omit(data1$kgs)
## D = 0.036423, p-value = 0.4047
## alternative hypothesis: two-sided
gamma_test(data1$kgs)
## 
##  Test of fit for the Gamma distribution
## 
## data:  data1$kgs
## V = -3.3776, p-value = 0.01692

Regression models

x=as.factor(data1$Covid)
levels(x)
## [1] "after state of alarm"           "Reference after state of alarm"
## [3] "Reference No"                   "Reference state of alarm 1"    
## [5] "Reference state of alarm 2"     "state of alarm 1"              
## [7] "state of alarm 2"
data0=subset(data1,data1$Covid=="Reference state of alarm 1" | data1$Covid=="state of alarm 1")
boxplot(data0$euros~data0$Covid, col = rgb(0, 0.5, 1, alpha = 0.5),xlab="Covid Category",ylab="Price")

boxplot(data0$kgs~data0$Covid, col = rgb(0, 0.5, 1, alpha = 0.5),xlab="Covid Category",ylab="Abundance")

x=as.factor(data0$Covid)
table(x)
## x
## Reference state of alarm 1           state of alarm 1 
##                         69                         68
y=as.numeric(data0$euros)
x=relevel(x, ref = "Reference state of alarm 1")
z=as.numeric(data0$kgs)

Model0<-glm((y)~x,family=Gamma(link=log))
cooksd <- cooks.distance(Model0)
influential <- cooksd[(cooksd > (3 * mean(cooksd, na.rm = TRUE)))]
influential=as.numeric(names(influential))
y=y[-influential]
xaux=x[-influential]
Model0<-glm((y)~xaux,family=Gamma(link=log))
summary(Model0)
## 
## Call:
## glm(formula = (y) ~ xaux, family = Gamma(link = log))
## 
## Deviance Residuals: 
##      Min        1Q    Median        3Q       Max  
## -0.23848  -0.09101  -0.01231   0.09145   0.22284  
## 
## Coefficients:
##                      Estimate Std. Error t value Pr(>|t|)    
## (Intercept)           3.47213    0.01554 223.444  < 2e-16 ***
## xauxstate of alarm 1 -0.16271    0.02147  -7.578 7.06e-12 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for Gamma family taken to be 0.01448802)
## 
##     Null deviance: 2.6263  on 125  degrees of freedom
## Residual deviance: 1.7930  on 124  degrees of freedom
## AIC: 679.78
## 
## Number of Fisher Scoring iterations: 4
Model0<-glm((z)~x,family=Gamma(link=log))
cooksd <- cooks.distance(Model0)
influential <- cooksd[(cooksd > (3 * mean(cooksd, na.rm = TRUE)))]
influential=as.numeric(names(influential))
z=z[-influential]
xaux=x[-influential]

Model0<-glm((z)~xaux,family=Gamma(link=log))
summary(Model0)
## 
## Call:
## glm(formula = (z) ~ xaux, family = Gamma(link = log))
## 
## Deviance Residuals: 
##      Min        1Q    Median        3Q       Max  
## -2.73246  -0.67443  -0.08934   0.34292   1.22281  
## 
## Coefficients:
##                      Estimate Std. Error t value Pr(>|t|)    
## (Intercept)           5.21510    0.08027  64.972  < 2e-16 ***
## xauxstate of alarm 1 -0.49081    0.11530  -4.257 3.98e-05 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for Gamma family taken to be 0.4316647)
## 
##     Null deviance: 94.161  on 129  degrees of freedom
## Residual deviance: 86.455  on 128  degrees of freedom
## AIC: 1543.7
## 
## Number of Fisher Scoring iterations: 5
data01=subset(data1,data1$Covid=="Reference state of alarm 2" | data1$Covid=="state of alarm 2")
boxplot(data01$euros~data01$Covid, col = rgb(0, 0.5, 1, alpha = 0.5),xlab="Covid Category",ylab="Price")

boxplot(data01$kgs~data01$Covid, col = rgb(0, 0.5, 1, alpha = 0.5),xlab="Covid Category",ylab="Abundance")

x=as.factor(data01$Covid)
table(x)
## x
## Reference state of alarm 2           state of alarm 2 
##                         98                        135
y=as.numeric(data01$euros)
x=relevel(x, ref = "Reference state of alarm 2")
z=as.numeric(data01$kgs)


Model01<-glm((y)~x,family=Gamma(link=log))
cooksd <- cooks.distance(Model01)
influential <- cooksd[(cooksd > (3 * mean(cooksd, na.rm = TRUE)))]
influential=as.numeric(names(influential))
y=y[-influential]
xaux=x[-influential]
Model01<-glm((y)~xaux,family=Gamma(link=log))
summary(Model01)
## 
## Call:
## glm(formula = (y) ~ xaux, family = Gamma(link = log))
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -0.4028  -0.1509  -0.0345   0.1218   0.4369  
## 
## Coefficients:
##                      Estimate Std. Error t value Pr(>|t|)    
## (Intercept)           3.62752    0.02060 176.071  < 2e-16 ***
## xauxstate of alarm 2  0.11259    0.02718   4.143 4.95e-05 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for Gamma family taken to be 0.03862639)
## 
##     Null deviance: 8.5676  on 213  degrees of freedom
## Residual deviance: 7.9085  on 212  degrees of freedom
## AIC: 1481.2
## 
## Number of Fisher Scoring iterations: 4
Model01<-glm((z)~x,family=Gamma(link=log))
cooksd <- cooks.distance(Model01)
influential <- cooksd[(cooksd > (3 * mean(cooksd, na.rm = TRUE)))]
influential=as.numeric(names(influential))
z=z[-influential]
xaux=x[-influential]

Model01<-glm((z)~xaux,family=Gamma(link=log))
summary(Model01)
## 
## Call:
## glm(formula = (z) ~ xaux, family = Gamma(link = log))
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -2.5360  -0.8295  -0.1895   0.3631   1.3471  
## 
## Coefficients:
##                      Estimate Std. Error t value Pr(>|t|)    
## (Intercept)           4.50797    0.08212  54.895   <2e-16 ***
## xauxstate of alarm 2  0.04342    0.10710   0.405    0.686    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for Gamma family taken to be 0.6001799)
## 
##     Null deviance: 172.63  on 215  degrees of freedom
## Residual deviance: 172.53  on 214  degrees of freedom
## AIC: 2384.6
## 
## Number of Fisher Scoring iterations: 5
data02=subset(data1,data1$Covid=="Reference after state of alarm" | data1$Covid=="after state of alarm")
boxplot(data02$euros~data02$Covid, col = rgb(0, 0.5, 1, alpha = 0.5),xlab="Covid Category",ylab="Price")

boxplot(data02$kgs~data02$Covid, col = rgb(0, 0.5, 1, alpha = 0.5),xlab="Covid Category",ylab="Abundance")

x=as.factor(data02$Covid)
table(x)
## x
##           after state of alarm Reference after state of alarm 
##                             89                             90
y=as.numeric(data02$euros)
x=relevel(x, ref = "Reference after state of alarm")
z=as.numeric(data02$kgs)

Model02<-glm((y)~x,family=Gamma(link=log))
cooksd <- cooks.distance(Model02)
influential <- cooksd[(cooksd > (3 * mean(cooksd, na.rm = TRUE)))]
influential=as.numeric(names(influential))
y=y[-influential]
xaux=x[-influential]
Model02<-glm((y)~xaux,family=Gamma(link=log))
summary(Model02)
## 
## Call:
## glm(formula = (y) ~ xaux, family = Gamma(link = log))
## 
## Deviance Residuals: 
##      Min        1Q    Median        3Q       Max  
## -0.24662  -0.09473   0.01281   0.08260   0.23725  
## 
## Coefficients:
##                          Estimate Std. Error t value Pr(>|t|)    
## (Intercept)               3.63379    0.01299   279.8   <2e-16 ***
## xauxafter state of alarm  0.01865    0.01865     1.0    0.319    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for Gamma family taken to be 0.01433953)
## 
##     Null deviance: 2.3843  on 164  degrees of freedom
## Residual deviance: 2.3700  on 163  degrees of freedom
## AIC: 974.3
## 
## Number of Fisher Scoring iterations: 4
Model02<-glm((z)~x,family=Gamma(link=log))
cooksd <- cooks.distance(Model02)
influential <- cooksd[(cooksd > (3 * mean(cooksd, na.rm = TRUE)))]
influential=as.numeric(names(influential))
z=z[-influential]
xaux=x[-influential]

Model02<-glm((z)~xaux,family=Gamma(link=log))
summary(Model02)
## 
## Call:
## glm(formula = (z) ~ xaux, family = Gamma(link = log))
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -3.1337  -0.6933  -0.1445   0.3300   1.0643  
## 
## Coefficients:
##                          Estimate Std. Error t value Pr(>|t|)    
## (Intercept)               4.70346    0.07248  64.889   <2e-16 ***
## xauxafter state of alarm  0.11923    0.10251   1.163    0.246    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for Gamma family taken to be 0.4360805)
## 
##     Null deviance: 101.73  on 165  degrees of freedom
## Residual deviance: 101.14  on 164  degrees of freedom
## AIC: 1891.7
## 
## Number of Fisher Scoring iterations: 5

3.10 Scorpaena scrofa

data1=subset(data_M,data_M$specie=="RSE-CABRACHO" )

Is gamma a correct distribution for the response variable PRICE?

summary(data1$euros)
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##   1.279  10.253  12.691  12.571  14.898  26.800
a=fitdist(data1$euros, distr = "gamma",start=list(shape = 1, rate = 2), lower = -1,method = "mme")
plot(a)

ks.test(na.omit(data1$euros), "pgamma", a$estimate[1],a$estimate[2])
## 
##  One-sample Kolmogorov-Smirnov test
## 
## data:  na.omit(data1$euros)
## D = 0.05449, p-value = 0.0483
## alternative hypothesis: two-sided
gamma_test(data1$euros)
## 
##  Test of fit for the Gamma distribution
## 
## data:  data1$euros
## V = -8.2052, p-value = 6.555e-09

Is gamma a correct distribution for the response variable ABUNDANCE?

summary(data1$kgs)
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##    0.36   16.56   31.03   58.63   63.69  595.93
a=fitdist(data1$kgs, distr = "gamma",start=list(shape = 1, rate = 2), lower = -1,method = "mme")
plot(a)

ks.test(na.omit(data1$kgs), "pgamma", a$estimate[1],a$estimate[2])
## 
##  One-sample Kolmogorov-Smirnov test
## 
## data:  na.omit(data1$kgs)
## D = 0.16662, p-value = 1.554e-15
## alternative hypothesis: two-sided
gamma_test(data1$kgs)
## 
##  Test of fit for the Gamma distribution
## 
## data:  data1$kgs
## V = 11.298, p-value = 1.359e-15

Regression models

x=as.factor(data1$Covid)
levels(x)
## [1] "after state of alarm"           "Reference after state of alarm"
## [3] "Reference No"                   "Reference state of alarm 1"    
## [5] "Reference state of alarm 2"     "state of alarm 1"              
## [7] "state of alarm 2"
data0=subset(data1,data1$Covid=="Reference state of alarm 1" | data1$Covid=="state of alarm 1")
boxplot(data0$euros~data0$Covid, col = rgb(0, 0.5, 1, alpha = 0.5),xlab="Covid Category",ylab="Price")

boxplot(data0$kgs~data0$Covid, col = rgb(0, 0.5, 1, alpha = 0.5),xlab="Covid Category",ylab="Abundance")

x=as.factor(data0$Covid)
table(x)
## x
## Reference state of alarm 1           state of alarm 1 
##                         71                         70
y=as.numeric(data0$euros)
x=relevel(x, ref = "Reference state of alarm 1")
z=as.numeric(data0$kgs)

Model0<-glm((y)~x,family=Gamma(link=log))
cooksd <- cooks.distance(Model0)
influential <- cooksd[(cooksd > (3 * mean(cooksd, na.rm = TRUE)))]
influential=as.numeric(names(influential))
y=y[-influential]
xaux=x[-influential]
Model0<-glm((y)~xaux,family=Gamma(link=log))
summary(Model0)
## 
## Call:
## glm(formula = (y) ~ xaux, family = Gamma(link = log))
## 
## Deviance Residuals: 
##      Min        1Q    Median        3Q       Max  
## -0.53058  -0.17892  -0.01259   0.16029   0.35482  
## 
## Coefficients:
##                      Estimate Std. Error t value Pr(>|t|)    
## (Intercept)           2.53288    0.02577  98.298  < 2e-16 ***
## xauxstate of alarm 1 -0.10961    0.03616  -3.031  0.00296 ** 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for Gamma family taken to be 0.04182974)
## 
##     Null deviance: 5.9466  on 127  degrees of freedom
## Residual deviance: 5.5621  on 126  degrees of freedom
## AIC: 597.37
## 
## Number of Fisher Scoring iterations: 4
Model0<-glm((z)~x,family=Gamma(link=log))
cooksd <- cooks.distance(Model0)
influential <- cooksd[(cooksd > (3 * mean(cooksd, na.rm = TRUE)))]
influential=as.numeric(names(influential))
z=z[-influential]
xaux=x[-influential]

Model0<-glm((z)~xaux,family=Gamma(link=log))
summary(Model0)
## 
## Call:
## glm(formula = (z) ~ xaux, family = Gamma(link = log))
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -2.6044  -0.6992  -0.1027   0.3422   1.3775  
## 
## Coefficients:
##                      Estimate Std. Error t value Pr(>|t|)    
## (Intercept)           3.59897    0.08325  43.229   <2e-16 ***
## xauxstate of alarm 1 -0.24167    0.11956  -2.021   0.0453 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for Gamma family taken to be 0.4713207)
## 
##     Null deviance: 80.329  on 131  degrees of freedom
## Residual deviance: 78.413  on 130  degrees of freedom
## AIC: 1165.6
## 
## Number of Fisher Scoring iterations: 5
data01=subset(data1,data1$Covid=="Reference state of alarm 2" | data1$Covid=="state of alarm 2")
boxplot(data01$euros~data01$Covid, col = rgb(0, 0.5, 1, alpha = 0.5),xlab="Covid Category",ylab="Price")

boxplot(data01$kgs~data01$Covid, col = rgb(0, 0.5, 1, alpha = 0.5),xlab="Covid Category",ylab="Abundance")

x=as.factor(data01$Covid)
table(x)
## x
## Reference state of alarm 2           state of alarm 2 
##                        105                        138
y=as.numeric(data01$euros)
x=relevel(x, ref = "Reference state of alarm 2")
z=as.numeric(data01$kgs)


Model01<-glm((y)~x,family=Gamma(link=log))
cooksd <- cooks.distance(Model01)
influential <- cooksd[(cooksd > (3 * mean(cooksd, na.rm = TRUE)))]
influential=as.numeric(names(influential))
y=y[-influential]
xaux=x[-influential]
Model01<-glm((y)~xaux,family=Gamma(link=log))
summary(Model01)
## 
## Call:
## glm(formula = (y) ~ xaux, family = Gamma(link = log))
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -0.7220  -0.1588   0.0124   0.1473   0.4728  
## 
## Coefficients:
##                       Estimate Std. Error t value Pr(>|t|)    
## (Intercept)           2.483112   0.026360  94.200   <2e-16 ***
## xauxstate of alarm 2 -0.006816   0.033955  -0.201    0.841    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for Gamma family taken to be 0.06184125)
## 
##     Null deviance: 15.113  on 223  degrees of freedom
## Residual deviance: 15.110  on 222  degrees of freedom
## AIC: 1135.7
## 
## Number of Fisher Scoring iterations: 4
Model01<-glm((z)~x,family=Gamma(link=log))
cooksd <- cooks.distance(Model01)
influential <- cooksd[(cooksd > (3 * mean(cooksd, na.rm = TRUE)))]
influential=as.numeric(names(influential))
z=z[-influential]
xaux=x[-influential]

Model01<-glm((z)~xaux,family=Gamma(link=log))
summary(Model01)
## 
## Call:
## glm(formula = (z) ~ xaux, family = Gamma(link = log))
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -2.5407  -0.9355  -0.3465   0.3309   1.9228  
## 
## Coefficients:
##                      Estimate Std. Error t value Pr(>|t|)    
## (Intercept)           3.47648    0.09355  37.160  < 2e-16 ***
## xauxstate of alarm 2  0.37952    0.12508   3.034  0.00269 ** 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for Gamma family taken to be 0.8752456)
## 
##     Null deviance: 223.97  on 226  degrees of freedom
## Residual deviance: 216.07  on 225  degrees of freedom
## AIC: 2132.8
## 
## Number of Fisher Scoring iterations: 6
data02=subset(data1,data1$Covid=="Reference after state of alarm" | data1$Covid=="after state of alarm")
boxplot(data02$euros~data02$Covid, col = rgb(0, 0.5, 1, alpha = 0.5),xlab="Covid Category",ylab="Price")

boxplot(data02$kgs~data02$Covid, col = rgb(0, 0.5, 1, alpha = 0.5),xlab="Covid Category",ylab="Abundance")

x=as.factor(data02$Covid)
table(x)
## x
##           after state of alarm Reference after state of alarm 
##                             96                             96
y=as.numeric(data02$euros)
x=relevel(x, ref = "Reference after state of alarm")
z=as.numeric(data02$kgs)

Model02<-glm((y)~x,family=Gamma(link=log))
cooksd <- cooks.distance(Model02)
influential <- cooksd[(cooksd > (3 * mean(cooksd, na.rm = TRUE)))]
influential=as.numeric(names(influential))
y=y[-influential]
xaux=x[-influential]
Model02<-glm((y)~xaux,family=Gamma(link=log))
summary(Model02)
## 
## Call:
## glm(formula = (y) ~ xaux, family = Gamma(link = log))
## 
## Deviance Residuals: 
##      Min        1Q    Median        3Q       Max  
## -0.60633  -0.15409   0.01408   0.14523   0.36990  
## 
## Coefficients:
##                           Estimate Std. Error t value Pr(>|t|)    
## (Intercept)               2.633568   0.023292 113.070   <2e-16 ***
## xauxafter state of alarm -0.002776   0.032386  -0.086    0.932    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for Gamma family taken to be 0.04556973)
## 
##     Null deviance: 8.7374  on 173  degrees of freedom
## Residual deviance: 8.7371  on 172  degrees of freedom
## AIC: 887.97
## 
## Number of Fisher Scoring iterations: 4
Model02<-glm((z)~x,family=Gamma(link=log))
cooksd <- cooks.distance(Model02)
influential <- cooksd[(cooksd > (3 * mean(cooksd, na.rm = TRUE)))]
influential=as.numeric(names(influential))
z=z[-influential]
xaux=x[-influential]

Model02<-glm((z)~xaux,family=Gamma(link=log))
summary(Model02)
## 
## Call:
## glm(formula = (z) ~ xaux, family = Gamma(link = log))
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -2.3993  -0.9183  -0.4366   0.3653   2.0750  
## 
## Coefficients:
##                          Estimate Std. Error t value Pr(>|t|)    
## (Intercept)                3.8355     0.0997  38.470  < 2e-16 ***
## xauxafter state of alarm   0.5983     0.1406   4.255 3.36e-05 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for Gamma family taken to be 0.8946417)
## 
##     Null deviance: 182.69  on 180  degrees of freedom
## Residual deviance: 166.74  on 179  degrees of freedom
## AIC: 1862.6
## 
## Number of Fisher Scoring iterations: 6

3.11 Xiphias gladius

data1=subset(data_M,data_M$specie=="SWO-PEZ ESPADA O EMPERADOR" )

Is gamma a correct distribution for the response variable PRICE?

summary(data1$euros)
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##   4.565   6.520   6.994   7.213   7.871  11.000
a=fitdist(data1$euros, distr = "gamma",start=list(shape = 1, rate = 2), lower = -1,method = "mme")
plot(a)

ks.test(na.omit(data1$euros), "pgamma", a$estimate[1],a$estimate[2])
## 
##  One-sample Kolmogorov-Smirnov test
## 
## data:  na.omit(data1$euros)
## D = 0.11394, p-value = 0.0002643
## alternative hypothesis: two-sided
gamma_test(data1$euros)
## 
##  Test of fit for the Gamma distribution
## 
## data:  data1$euros
## V = 3.9071, p-value = 0.005732

Is gamma a correct distribution for the response variable ABUNDANCE?

summary(data1$kgs)
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##    23.5   633.1  1898.3  3226.6  4024.4 35299.5
a=fitdist(data1$kgs, distr = "gamma",start=list(shape = 1, rate = 2), lower = -1,method = "mme")
plot(a)

ks.test(na.omit(data1$kgs), "pgamma", a$estimate[1],a$estimate[2])
## 
##  One-sample Kolmogorov-Smirnov test
## 
## data:  na.omit(data1$kgs)
## D = 0.083019, p-value = 0.01745
## alternative hypothesis: two-sided
gamma_test(data1$kgs)
## 
##  Test of fit for the Gamma distribution
## 
## data:  data1$kgs
## V = 4.1034, p-value = 0.003713

Regression models

x=as.factor(data1$Covid)
levels(x)
## [1] "after state of alarm"           "Reference after state of alarm"
## [3] "Reference state of alarm 1"     "Reference state of alarm 2"    
## [5] "state of alarm 1"               "state of alarm 2"
data0=subset(data1,data1$Covid=="Reference state of alarm 1" | data1$Covid=="state of alarm 1")
boxplot(data0$euros~data0$Covid, col = rgb(0, 0.5, 1, alpha = 0.5),xlab="Covid Category",ylab="Price")

boxplot(data0$kgs~data0$Covid, col = rgb(0, 0.5, 1, alpha = 0.5),xlab="Covid Category",ylab="Abundance")

x=as.factor(data0$Covid)
table(x)
## x
## Reference state of alarm 1           state of alarm 1 
##                         26                         24
y=as.numeric(data0$euros)
x=relevel(x, ref = "Reference state of alarm 1")
z=as.numeric(data0$kgs)

Model0<-glm((y)~x,family=Gamma(link=log))
cooksd <- cooks.distance(Model0)
influential <- cooksd[(cooksd > (3 * mean(cooksd, na.rm = TRUE)))]
influential=as.numeric(names(influential))
y=y[-influential]
xaux=x[-influential]
Model0<-glm((y)~xaux,family=Gamma(link=log))
summary(Model0)
## 
## Call:
## glm(formula = (y) ~ xaux, family = Gamma(link = log))
## 
## Deviance Residuals: 
##       Min         1Q     Median         3Q        Max  
## -0.178913  -0.079903   0.006131   0.080356   0.188189  
## 
## Coefficients:
##                      Estimate Std. Error t value Pr(>|t|)    
## (Intercept)           2.13299    0.02105 101.326  < 2e-16 ***
## xauxstate of alarm 1 -0.14180    0.02977  -4.763 2.29e-05 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for Gamma family taken to be 0.009748922)
## 
##     Null deviance: 0.62981  on 43  degrees of freedom
## Residual deviance: 0.40882  on 42  degrees of freedom
## AIC: 106.13
## 
## Number of Fisher Scoring iterations: 4
Model0<-glm((z)~x,family=Gamma(link=log))
cooksd <- cooks.distance(Model0)
influential <- cooksd[(cooksd > (3 * mean(cooksd, na.rm = TRUE)))]
influential=as.numeric(names(influential))
z=z[-influential]
xaux=x[-influential]

Model0<-glm((z)~xaux,family=Gamma(link=log))
summary(Model0)
## 
## Call:
## glm(formula = (z) ~ xaux, family = Gamma(link = log))
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -2.4955  -0.8711  -0.3593   0.4480   1.6210  
## 
## Coefficients:
##                      Estimate Std. Error t value Pr(>|t|)    
## (Intercept)            6.9194     0.1773  39.030   <2e-16 ***
## xauxstate of alarm 1   0.3346     0.2564   1.305    0.199    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for Gamma family taken to be 0.7543237)
## 
##     Null deviance: 44.585  on 45  degrees of freedom
## Residual deviance: 43.299  on 44  degrees of freedom
## AIC: 748.81
## 
## Number of Fisher Scoring iterations: 5
data01=subset(data1,data1$Covid=="Reference state of alarm 2" | data1$Covid=="state of alarm 2")
boxplot(data01$euros~data01$Covid, col = rgb(0, 0.5, 1, alpha = 0.5),xlab="Covid Category",ylab="Price")

boxplot(data01$kgs~data01$Covid, col = rgb(0, 0.5, 1, alpha = 0.5),xlab="Covid Category",ylab="Abundance")

x=as.factor(data01$Covid)
table(x)
## x
## Reference state of alarm 2           state of alarm 2 
##                         39                         50
y=as.numeric(data01$euros)
x=relevel(x, ref = "Reference state of alarm 2")
z=as.numeric(data01$kgs)


Model01<-glm((y)~x,family=Gamma(link=log))
cooksd <- cooks.distance(Model01)
influential <- cooksd[(cooksd > (3 * mean(cooksd, na.rm = TRUE)))]
influential=as.numeric(names(influential))
y=y[-influential]
xaux=x[-influential]
Model01<-glm((y)~xaux,family=Gamma(link=log))
summary(Model01)
## 
## Call:
## glm(formula = (y) ~ xaux, family = Gamma(link = log))
## 
## Deviance Residuals: 
##      Min        1Q    Median        3Q       Max  
## -0.31549  -0.13805   0.02454   0.11745   0.27988  
## 
## Coefficients:
##                      Estimate Std. Error t value Pr(>|t|)    
## (Intercept)           2.10572    0.02613  80.576   <2e-16 ***
## xauxstate of alarm 2 -0.03593    0.03478  -1.033    0.304    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for Gamma family taken to be 0.02526896)
## 
##     Null deviance: 2.1952  on 84  degrees of freedom
## Residual deviance: 2.1682  on 83  degrees of freedom
## AIC: 288.09
## 
## Number of Fisher Scoring iterations: 4
Model01<-glm((z)~x,family=Gamma(link=log))
cooksd <- cooks.distance(Model01)
influential <- cooksd[(cooksd > (3 * mean(cooksd, na.rm = TRUE)))]
influential=as.numeric(names(influential))
z=z[-influential]
xaux=x[-influential]

Model01<-glm((z)~xaux,family=Gamma(link=log))
summary(Model01)
## 
## Call:
## glm(formula = (z) ~ xaux, family = Gamma(link = log))
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -2.2421  -1.2099  -0.4707   0.4608   1.8194  
## 
## Coefficients:
##                      Estimate Std. Error t value Pr(>|t|)    
## (Intercept)            6.6938     0.1667  40.159   <2e-16 ***
## xauxstate of alarm 2   0.3386     0.2215   1.529     0.13    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for Gamma family taken to be 1.000175)
## 
##     Null deviance: 99.642  on 82  degrees of freedom
## Residual deviance: 97.350  on 81  degrees of freedom
## AIC: 1316.1
## 
## Number of Fisher Scoring iterations: 6
data02=subset(data1,data1$Covid=="Reference after state of alarm" | data1$Covid=="after state of alarm")
boxplot(data02$euros~data02$Covid, col = rgb(0, 0.5, 1, alpha = 0.5),xlab="Covid Category",ylab="Price")

boxplot(data02$kgs~data02$Covid, col = rgb(0, 0.5, 1, alpha = 0.5),xlab="Covid Category",ylab="Abundance")

x=as.factor(data02$Covid)
table(x)
## x
##           after state of alarm Reference after state of alarm 
##                             97                            108
y=as.numeric(data02$euros)
x=relevel(x, ref = "Reference after state of alarm")
z=as.numeric(data02$kgs)

Model02<-glm((y)~x,family=Gamma(link=log))
cooksd <- cooks.distance(Model02)
influential <- cooksd[(cooksd > (3 * mean(cooksd, na.rm = TRUE)))]
influential=as.numeric(names(influential))
y=y[-influential]
xaux=x[-influential]
Model02<-glm((y)~xaux,family=Gamma(link=log))
summary(Model02)
## 
## Call:
## glm(formula = (y) ~ xaux, family = Gamma(link = log))
## 
## Deviance Residuals: 
##      Min        1Q    Median        3Q       Max  
## -0.20067  -0.04310   0.01264   0.04741   0.16473  
## 
## Coefficients:
##                          Estimate Std. Error t value Pr(>|t|)    
## (Intercept)              1.897444   0.007579 250.365   <2e-16 ***
## xauxafter state of alarm 0.024952   0.011197   2.228   0.0271 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for Gamma family taken to be 0.005571377)
## 
##     Null deviance: 1.0481  on 178  degrees of freedom
## Residual deviance: 1.0204  on 177  degrees of freedom
## AIC: 271.57
## 
## Number of Fisher Scoring iterations: 3
Model02<-glm((z)~x,family=Gamma(link=log))
cooksd <- cooks.distance(Model02)
influential <- cooksd[(cooksd > (3 * mean(cooksd, na.rm = TRUE)))]
influential=as.numeric(names(influential))
z=z[-influential]
xaux=x[-influential]

Model02<-glm((z)~xaux,family=Gamma(link=log))
summary(Model02)
## 
## Call:
## glm(formula = (z) ~ xaux, family = Gamma(link = log))
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -2.7619  -0.8076  -0.2752   0.3519   1.5695  
## 
## Coefficients:
##                          Estimate Std. Error t value Pr(>|t|)    
## (Intercept)               8.08977    0.08375  96.589   <2e-16 ***
## xauxafter state of alarm  0.11493    0.12134   0.947    0.345    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for Gamma family taken to be 0.7014849)
## 
##     Null deviance: 171.08  on 190  degrees of freedom
## Residual deviance: 170.45  on 189  degrees of freedom
## AIC: 3494.8
## 
## Number of Fisher Scoring iterations: 5

3.12 Caranx rhonchus

data1=subset(data_M,data_M$specie=="HMY-JURELA O JUREL DORADO" )

Is gamma a correct distribution for the response variable PRICE?

summary(data1$euros)
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##  0.5003  2.6803  3.7075  4.0152  5.1932 17.1991
a=fitdist(data1$euros, distr = "gamma",start=list(shape = 1, rate = 2), lower = -1,method = "mme")
plot(a)

ks.test(na.omit(data1$euros), "pgamma", a$estimate[1],a$estimate[2])
## 
##  One-sample Kolmogorov-Smirnov test
## 
## data:  na.omit(data1$euros)
## D = 0.042033, p-value = 0.7267
## alternative hypothesis: two-sided
gamma_test(data1$euros)
## 
##  Test of fit for the Gamma distribution
## 
## data:  data1$euros
## V = 3.7781, p-value = 0.007551

Is gamma a correct distribution for the response variable ABUNDANCE?

summary(data1$kgs)
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##   0.340   3.312   7.350  25.221  20.200 673.120
a=fitdist(data1$kgs, distr = "gamma",start=list(shape = 1, rate = 2), lower = -1,method = "mme")
plot(a)

ks.test(na.omit(data1$kgs), "pgamma", a$estimate[1],a$estimate[2])
## 
##  One-sample Kolmogorov-Smirnov test
## 
## data:  na.omit(data1$kgs)
## D = 0.41401, p-value < 2.2e-16
## alternative hypothesis: two-sided
gamma_test(data1$kgs)
## 
##  Test of fit for the Gamma distribution
## 
## data:  data1$kgs
## V = 17.605, p-value < 2.2e-16

Regression models

x=as.factor(data1$Covid)
levels(x)
## [1] "after state of alarm"           "Reference after state of alarm"
## [3] "Reference No"                   "Reference state of alarm 1"    
## [5] "Reference state of alarm 2"     "state of alarm 1"              
## [7] "state of alarm 2"
data0=subset(data1,data1$Covid=="Reference state of alarm 1" | data1$Covid=="state of alarm 1")
boxplot(data0$euros~data0$Covid, col = rgb(0, 0.5, 1, alpha = 0.5),xlab="Covid Category",ylab="Price")

boxplot(data0$kgs~data0$Covid, col = rgb(0, 0.5, 1, alpha = 0.5),xlab="Covid Category",ylab="Abundance")

x=as.factor(data0$Covid)
table(x)
## x
## Reference state of alarm 1           state of alarm 1 
##                         25                         22
y=as.numeric(data0$euros)
x=relevel(x, ref = "Reference state of alarm 1")
z=as.numeric(data0$kgs)

Model0<-glm((y)~x,family=Gamma(link=log))
cooksd <- cooks.distance(Model0)
influential <- cooksd[(cooksd > (3 * mean(cooksd, na.rm = TRUE)))]
influential=as.numeric(names(influential))
y=y[-influential]
xaux=x[-influential]
Model0<-glm((y)~xaux,family=Gamma(link=log))
summary(Model0)
## 
## Call:
## glm(formula = (y) ~ xaux, family = Gamma(link = log))
## 
## Deviance Residuals: 
##      Min        1Q    Median        3Q       Max  
## -1.14237  -0.24879   0.00317   0.25757   0.64688  
## 
## Coefficients:
##                      Estimate Std. Error t value Pr(>|t|)    
## (Intercept)           1.56483    0.07996  19.570   <2e-16 ***
## xauxstate of alarm 1 -0.09001    0.11562  -0.778     0.44    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for Gamma family taken to be 0.1534437)
## 
##     Null deviance: 8.9850  on 45  degrees of freedom
## Residual deviance: 8.8922  on 44  degrees of freedom
## AIC: 193.53
## 
## Number of Fisher Scoring iterations: 4
Model0<-glm((z)~x,family=Gamma(link=log))
cooksd <- cooks.distance(Model0)
influential <- cooksd[(cooksd > (3 * mean(cooksd, na.rm = TRUE)))]
influential=as.numeric(names(influential))
z=z[-influential]
xaux=x[-influential]

Model0<-glm((z)~xaux,family=Gamma(link=log))
summary(Model0)
## 
## Call:
## glm(formula = (z) ~ xaux, family = Gamma(link = log))
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -1.9382  -0.6645  -0.1684   0.3758   1.4702  
## 
## Coefficients:
##                      Estimate Std. Error t value Pr(>|t|)    
## (Intercept)            1.4632     0.1582   9.247 1.39e-11 ***
## xauxstate of alarm 1   0.2766     0.2264   1.222    0.229    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for Gamma family taken to be 0.5507682)
## 
##     Null deviance: 26.688  on 42  degrees of freedom
## Residual deviance: 25.866  on 41  degrees of freedom
## AIC: 222.02
## 
## Number of Fisher Scoring iterations: 5
data01=subset(data1,data1$Covid=="Reference state of alarm 2" | data1$Covid=="state of alarm 2")
boxplot(data01$euros~data01$Covid, col = rgb(0, 0.5, 1, alpha = 0.5),xlab="Covid Category",ylab="Price")

boxplot(data01$kgs~data01$Covid, col = rgb(0, 0.5, 1, alpha = 0.5),xlab="Covid Category",ylab="Abundance")

x=as.factor(data01$Covid)
table(x)
## x
## Reference state of alarm 2           state of alarm 2 
##                         43                         50
y=as.numeric(data01$euros)
x=relevel(x, ref = "Reference state of alarm 2")
z=as.numeric(data01$kgs)


Model01<-glm((y)~x,family=Gamma(link=log))
cooksd <- cooks.distance(Model01)
influential <- cooksd[(cooksd > (3 * mean(cooksd, na.rm = TRUE)))]
influential=as.numeric(names(influential))
y=y[-influential]
xaux=x[-influential]
Model01<-glm((y)~xaux,family=Gamma(link=log))
summary(Model01)
## 
## Call:
## glm(formula = (y) ~ xaux, family = Gamma(link = log))
## 
## Deviance Residuals: 
##      Min        1Q    Median        3Q       Max  
## -0.79033  -0.26249  -0.04324   0.23553   0.58890  
## 
## Coefficients:
##                      Estimate Std. Error t value Pr(>|t|)    
## (Intercept)           1.38207    0.04932  28.023   <2e-16 ***
## xauxstate of alarm 2 -0.01332    0.06684  -0.199    0.842    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for Gamma family taken to be 0.09972594)
## 
##     Null deviance: 9.2636  on 89  degrees of freedom
## Residual deviance: 9.2597  on 88  degrees of freedom
## AIC: 296.49
## 
## Number of Fisher Scoring iterations: 4
Model01<-glm((z)~x,family=Gamma(link=log))
cooksd <- cooks.distance(Model01)
influential <- cooksd[(cooksd > (3 * mean(cooksd, na.rm = TRUE)))]
influential=as.numeric(names(influential))
z=z[-influential]
xaux=x[-influential]

Model01<-glm((z)~xaux,family=Gamma(link=log))
summary(Model01)
## 
## Call:
## glm(formula = (z) ~ xaux, family = Gamma(link = log))
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -2.0332  -0.9881  -0.4768   0.1854   2.2096  
## 
## Coefficients:
##                      Estimate Std. Error t value Pr(>|t|)    
## (Intercept)            2.0912     0.1680   12.45   <2e-16 ***
## xauxstate of alarm 2   0.3821     0.2275    1.68   0.0966 .  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for Gamma family taken to be 1.128769)
## 
##     Null deviance: 85.631  on 87  degrees of freedom
## Residual deviance: 82.500  on 86  degrees of freedom
## AIC: 585.7
## 
## Number of Fisher Scoring iterations: 6
data02=subset(data1,data1$Covid=="Reference after state of alarm" | data1$Covid=="after state of alarm")
boxplot(data02$euros~data02$Covid, col = rgb(0, 0.5, 1, alpha = 0.5),xlab="Covid Category",ylab="Price")

boxplot(data02$kgs~data02$Covid, col = rgb(0, 0.5, 1, alpha = 0.5),xlab="Covid Category",ylab="Abundance")

x=as.factor(data02$Covid)
table(x)
## x
##           after state of alarm Reference after state of alarm 
##                             61                             59
y=as.numeric(data02$euros)
x=relevel(x, ref = "Reference after state of alarm")
z=as.numeric(data02$kgs)

Model02<-glm((y)~x,family=Gamma(link=log))
cooksd <- cooks.distance(Model02)
influential <- cooksd[(cooksd > (3 * mean(cooksd, na.rm = TRUE)))]
influential=as.numeric(names(influential))
y=y[-influential]
xaux=x[-influential]
Model02<-glm((y)~xaux,family=Gamma(link=log))
summary(Model02)
## 
## Call:
## glm(formula = (y) ~ xaux, family = Gamma(link = log))
## 
## Deviance Residuals: 
##      Min        1Q    Median        3Q       Max  
## -1.06377  -0.30958  -0.08618   0.20268   0.70374  
## 
## Coefficients:
##                           Estimate Std. Error t value Pr(>|t|)    
## (Intercept)               1.210671   0.055326  21.882   <2e-16 ***
## xauxafter state of alarm -0.005795   0.077207  -0.075     0.94    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for Gamma family taken to be 0.1652933)
## 
##     Null deviance: 18.657  on 110  degrees of freedom
## Residual deviance: 18.656  on 109  degrees of freedom
## AIC: 375.61
## 
## Number of Fisher Scoring iterations: 4
Model02<-glm((z)~x,family=Gamma(link=log))
cooksd <- cooks.distance(Model02)
influential <- cooksd[(cooksd > (3 * mean(cooksd, na.rm = TRUE)))]
influential=as.numeric(names(influential))
z=z[-influential]
xaux=x[-influential]

Model02<-glm((z)~xaux,family=Gamma(link=log))
summary(Model02)
## 
## Call:
## glm(formula = (z) ~ xaux, family = Gamma(link = log))
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -2.5353  -1.4124  -0.6162   0.2110   2.7239  
## 
## Coefficients:
##                          Estimate Std. Error t value Pr(>|t|)    
## (Intercept)               3.23119    0.16414  19.685   <2e-16 ***
## xauxafter state of alarm -0.06659    0.23419  -0.284    0.777    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for Gamma family taken to be 1.562663)
## 
##     Null deviance: 177.44  on 113  degrees of freedom
## Residual deviance: 177.31  on 112  degrees of freedom
## AIC: 959.73
## 
## Number of Fisher Scoring iterations: 6

3.13 Boops boops

data1=subset(data_M,data_M$specie=="BOG-BOGA" )

Is gamma a correct distribution for the response variable PRICE?

summary(data1$euros)
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##  0.0000  0.2450  0.2822  0.3962  0.4372  3.6466
a=fitdist(data1$euros, distr = "gamma",start=list(shape = 1, rate = 2), lower = -1,method = "mme")
plot(a)

ks.test(na.omit(data1$euros), "pgamma", a$estimate[1],a$estimate[2])
## 
##  One-sample Kolmogorov-Smirnov test
## 
## data:  na.omit(data1$euros)
## D = 0.22435, p-value < 2.2e-16
## alternative hypothesis: two-sided
gamma_test(data1$euros)
## 
##  Test of fit for the Gamma distribution
## 
## data:  data1$euros
## V = NaN, p-value = NA

Is gamma a correct distribution for the response variable ABUNDANCE?

summary(data1$kgs)
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##     0.2    36.2   283.3  1455.3  1308.5 17702.1
a=fitdist(data1$kgs, distr = "gamma",start=list(shape = 1, rate = 2), lower = -1,method = "mme")
plot(a)

ks.test(na.omit(data1$kgs), "pgamma", a$estimate[1],a$estimate[2])
## 
##  One-sample Kolmogorov-Smirnov test
## 
## data:  na.omit(data1$kgs)
## D = 0.12241, p-value = 2.313e-07
## alternative hypothesis: two-sided
gamma_test(data1$kgs)
## 
##  Test of fit for the Gamma distribution
## 
## data:  data1$kgs
## V = 2.8123, p-value = 0.04675

Regression models

ind=which(data1$euros==0)
if(length(ind)>0){data1=data1[-ind,]}

x=as.factor(data1$Covid)
levels(x)
## [1] "after state of alarm"           "Reference after state of alarm"
## [3] "Reference No"                   "Reference state of alarm 1"    
## [5] "Reference state of alarm 2"     "state of alarm 1"              
## [7] "state of alarm 2"
data0=subset(data1,data1$Covid=="Reference state of alarm 1" | data1$Covid=="state of alarm 1")
boxplot(data0$euros~data0$Covid, col = rgb(0, 0.5, 1, alpha = 0.5),xlab="Covid Category",ylab="Price")

boxplot(data0$kgs~data0$Covid, col = rgb(0, 0.5, 1, alpha = 0.5),xlab="Covid Category",ylab="Abundance")

x=as.factor(data0$Covid)
table(x)
## x
## Reference state of alarm 1           state of alarm 1 
##                         66                         65
y=as.numeric(data0$euros)
x=relevel(x, ref = "Reference state of alarm 1")
z=as.numeric(data0$kgs)

Model0<-glm((y)~x,family=Gamma(link=log))
cooksd <- cooks.distance(Model0)
influential <- cooksd[(cooksd > (3 * mean(cooksd, na.rm = TRUE)))]
influential=as.numeric(names(influential))
y=y[-influential]
xaux=x[-influential]
Model0<-glm((y)~xaux,family=Gamma(link=log))
summary(Model0)
## 
## Call:
## glm(formula = (y) ~ xaux, family = Gamma(link = log))
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -2.4813  -0.4396  -0.1621   0.3408   0.9697  
## 
## Coefficients:
##                      Estimate Std. Error t value Pr(>|t|)    
## (Intercept)          -0.80590    0.07486 -10.766   <2e-16 ***
## xauxstate of alarm 1 -0.01952    0.10628  -0.184    0.855    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for Gamma family taken to be 0.3586374)
## 
##     Null deviance: 68.327  on 126  degrees of freedom
## Residual deviance: 68.315  on 125  degrees of freedom
## AIC: 23.473
## 
## Number of Fisher Scoring iterations: 5
Model0<-glm((z)~x,family=Gamma(link=log))
cooksd <- cooks.distance(Model0)
influential <- cooksd[(cooksd > (3 * mean(cooksd, na.rm = TRUE)))]
influential=as.numeric(names(influential))
z=z[-influential]
xaux=x[-influential]

Model0<-glm((z)~xaux,family=Gamma(link=log))
summary(Model0)
## 
## Call:
## glm(formula = (z) ~ xaux, family = Gamma(link = log))
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -3.1972  -1.6434  -0.8815   0.1838   3.7187  
## 
## Coefficients:
##                      Estimate Std. Error t value Pr(>|t|)    
## (Intercept)            5.0759     0.1903  26.673   <2e-16 ***
## xauxstate of alarm 1   0.3398     0.2691   1.263    0.209    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for Gamma family taken to be 2.245211)
## 
##     Null deviance: 291.66  on 123  degrees of freedom
## Residual deviance: 288.10  on 122  degrees of freedom
## AIC: 1518.6
## 
## Number of Fisher Scoring iterations: 7
data01=subset(data1,data1$Covid=="Reference state of alarm 2" | data1$Covid=="state of alarm 2")
boxplot(data01$euros~data01$Covid, col = rgb(0, 0.5, 1, alpha = 0.5),xlab="Covid Category",ylab="Price")

boxplot(data01$kgs~data01$Covid, col = rgb(0, 0.5, 1, alpha = 0.5),xlab="Covid Category",ylab="Abundance")

x=as.factor(data01$Covid)
table(x)
## x
## Reference state of alarm 2           state of alarm 2 
##                         86                        120
y=as.numeric(data01$euros)
x=relevel(x, ref = "Reference state of alarm 2")
z=as.numeric(data01$kgs)


Model01<-glm((y)~x,family=Gamma(link=log))
cooksd <- cooks.distance(Model01)
influential <- cooksd[(cooksd > (3 * mean(cooksd, na.rm = TRUE)))]
influential=as.numeric(names(influential))
y=y[-influential]
xaux=x[-influential]
Model01<-glm((y)~xaux,family=Gamma(link=log))
summary(Model01)
## 
## Call:
## glm(formula = (y) ~ xaux, family = Gamma(link = log))
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -2.1818  -0.1494  -0.1098   0.1030   0.9538  
## 
## Coefficients:
##                      Estimate Std. Error t value Pr(>|t|)    
## (Intercept)          -1.17314    0.04416 -26.564   <2e-16 ***
## xauxstate of alarm 2 -0.08722    0.05696  -1.531    0.127    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for Gamma family taken to be 0.14628)
## 
##     Null deviance: 33.777  on 187  degrees of freedom
## Residual deviance: 33.432  on 186  degrees of freedom
## AIC: -273.82
## 
## Number of Fisher Scoring iterations: 4
Model01<-glm((z)~x,family=Gamma(link=log))
cooksd <- cooks.distance(Model01)
influential <- cooksd[(cooksd > (3 * mean(cooksd, na.rm = TRUE)))]
influential=as.numeric(names(influential))
z=z[-influential]
xaux=x[-influential]

Model01<-glm((z)~xaux,family=Gamma(link=log))
summary(Model01)
## 
## Call:
## glm(formula = (z) ~ xaux, family = Gamma(link = log))
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -3.6274  -1.4745  -0.4168   0.5389   1.9318  
## 
## Coefficients:
##                      Estimate Std. Error t value Pr(>|t|)    
## (Intercept)            5.9692     0.1195   49.93   <2e-16 ***
## xauxstate of alarm 2   1.8640     0.1570   11.87   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for Gamma family taken to be 1.171779)
## 
##     Null deviance: 559.33  on 194  degrees of freedom
## Residual deviance: 424.84  on 193  degrees of freedom
## AIC: 3100.1
## 
## Number of Fisher Scoring iterations: 7
data02=subset(data1,data1$Covid=="Reference after state of alarm" | data1$Covid=="after state of alarm")
boxplot(data02$euros~data02$Covid, col = rgb(0, 0.5, 1, alpha = 0.5),xlab="Covid Category",ylab="Price")

boxplot(data02$kgs~data02$Covid, col = rgb(0, 0.5, 1, alpha = 0.5),xlab="Covid Category",ylab="Abundance")

x=as.factor(data02$Covid)
table(x)
## x
##           after state of alarm Reference after state of alarm 
##                             76                             70
y=as.numeric(data02$euros)
x=relevel(x, ref = "Reference after state of alarm")
z=as.numeric(data02$kgs)

Model02<-glm((y)~x,family=Gamma(link=log))
cooksd <- cooks.distance(Model02)
influential <- cooksd[(cooksd > (3 * mean(cooksd, na.rm = TRUE)))]
influential=as.numeric(names(influential))
y=y[-influential]
xaux=x[-influential]
Model02<-glm((y)~xaux,family=Gamma(link=log))
summary(Model02)
## 
## Call:
## glm(formula = (y) ~ xaux, family = Gamma(link = log))
## 
## Deviance Residuals: 
##      Min        1Q    Median        3Q       Max  
## -2.51389  -0.17813  -0.08711   0.08301   1.16149  
## 
## Coefficients:
##                          Estimate Std. Error t value Pr(>|t|)    
## (Intercept)              -1.10057    0.06033 -18.242  < 2e-16 ***
## xauxafter state of alarm -0.36761    0.08322  -4.417 2.03e-05 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for Gamma family taken to be 0.236587)
## 
##     Null deviance: 50.755  on 136  degrees of freedom
## Residual deviance: 46.136  on 135  degrees of freedom
## AIC: -147.29
## 
## Number of Fisher Scoring iterations: 5
Model02<-glm((z)~x,family=Gamma(link=log))
cooksd <- cooks.distance(Model02)
influential <- cooksd[(cooksd > (3 * mean(cooksd, na.rm = TRUE)))]
influential=as.numeric(names(influential))
z=z[-influential]
xaux=x[-influential]

Model02<-glm((z)~xaux,family=Gamma(link=log))
summary(Model02)
## 
## Call:
## glm(formula = (z) ~ xaux, family = Gamma(link = log))
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -3.6333  -2.3085  -1.1699   0.3083   2.3518  
## 
## Coefficients:
##                          Estimate Std. Error t value Pr(>|t|)    
## (Intercept)                6.6836     0.1768  37.804  < 2e-16 ***
## xauxafter state of alarm   0.8164     0.2438   3.349  0.00106 ** 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for Gamma family taken to be 2.000424)
## 
##     Null deviance: 483.38  on 134  degrees of freedom
## Residual deviance: 461.83  on 133  degrees of freedom
## AIC: 2078.9
## 
## Number of Fisher Scoring iterations: 9

3.14 Aristeus antennatus

data1=subset(data_M,data_M$specie=="ARA-GAMBA ROJA O RAYADO" )

Is gamma a correct distribution for the response variable PRICE?

summary(data1$euros)
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##   12.91   31.49   36.26   37.64   41.01   85.65
a=fitdist(data1$euros, distr = "gamma",start=list(shape = 1, rate = 2), lower = -1,method = "mme")
plot(a)

ks.test(na.omit(data1$euros), "pgamma", a$estimate[1],a$estimate[2])
## 
##  One-sample Kolmogorov-Smirnov test
## 
## data:  na.omit(data1$euros)
## D = 0.091154, p-value = 0.0002293
## alternative hypothesis: two-sided
gamma_test(data1$euros)
## 
##  Test of fit for the Gamma distribution
## 
## data:  data1$euros
## V = 9.6669, p-value = 8.17e-12

Is gamma a correct distribution for the response variable ABUNDANCE?

summary(data1$kgs)
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##    0.52  256.82  410.61  528.39  670.18 3131.05
a=fitdist(data1$kgs, distr = "gamma",start=list(shape = 1, rate = 2), lower = -1,method = "mme")
plot(a)

ks.test(na.omit(data1$kgs), "pgamma", a$estimate[1],a$estimate[2])
## 
##  One-sample Kolmogorov-Smirnov test
## 
## data:  na.omit(data1$kgs)
## D = 0.06116, p-value = 0.03366
## alternative hypothesis: two-sided
gamma_test(data1$kgs)
## 
##  Test of fit for the Gamma distribution
## 
## data:  data1$kgs
## V = -1.3604, p-value = 0.3361

Regression models

x=as.factor(data1$Covid)
levels(x)
## [1] "after state of alarm"           "Reference after state of alarm"
## [3] "Reference No"                   "Reference state of alarm 1"    
## [5] "Reference state of alarm 2"     "state of alarm 1"              
## [7] "state of alarm 2"
data0=subset(data1,data1$Covid=="Reference state of alarm 1" | data1$Covid=="state of alarm 1")
boxplot(data0$euros~data0$Covid, col = rgb(0, 0.5, 1, alpha = 0.5),xlab="Covid Category",ylab="Price")

boxplot(data0$kgs~data0$Covid, col = rgb(0, 0.5, 1, alpha = 0.5),xlab="Covid Category",ylab="Abundance")

x=as.factor(data0$Covid)
table(x)
## x
## Reference state of alarm 1           state of alarm 1 
##                         69                         58
y=as.numeric(data0$euros)
x=relevel(x, ref = "Reference state of alarm 1")
z=as.numeric(data0$kgs)

Model0<-glm((y)~x,family=Gamma(link=log))
cooksd <- cooks.distance(Model0)
influential <- cooksd[(cooksd > (3 * mean(cooksd, na.rm = TRUE)))]
influential=as.numeric(names(influential))
y=y[-influential]
xaux=x[-influential]
Model0<-glm((y)~xaux,family=Gamma(link=log))
summary(Model0)
## 
## Call:
## glm(formula = (y) ~ xaux, family = Gamma(link = log))
## 
## Deviance Residuals: 
##      Min        1Q    Median        3Q       Max  
## -0.48154  -0.11683  -0.02041   0.09077   0.41123  
## 
## Coefficients:
##                      Estimate Std. Error t value Pr(>|t|)    
## (Intercept)           3.56969    0.02144 166.493  < 2e-16 ***
## xauxstate of alarm 1 -0.19226    0.03223  -5.966 2.52e-08 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for Gamma family taken to be 0.0312592)
## 
##     Null deviance: 4.9109  on 121  degrees of freedom
## Residual deviance: 3.8081  on 120  degrees of freedom
## AIC: 776.33
## 
## Number of Fisher Scoring iterations: 4
Model0<-glm((z)~x,family=Gamma(link=log))
cooksd <- cooks.distance(Model0)
influential <- cooksd[(cooksd > (3 * mean(cooksd, na.rm = TRUE)))]
influential=as.numeric(names(influential))
z=z[-influential]
xaux=x[-influential]

Model0<-glm((z)~xaux,family=Gamma(link=log))
summary(Model0)
## 
## Call:
## glm(formula = (z) ~ xaux, family = Gamma(link = log))
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -2.9592  -0.6767  -0.1876   0.4194   1.2255  
## 
## Coefficients:
##                      Estimate Std. Error t value Pr(>|t|)    
## (Intercept)           6.08630    0.08877  68.565   <2e-16 ***
## xauxstate of alarm 1 -0.01945    0.13057  -0.149    0.882    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for Gamma family taken to be 0.5042935)
## 
##     Null deviance: 92.581  on 118  degrees of freedom
## Residual deviance: 92.570  on 117  degrees of freedom
## AIC: 1682.8
## 
## Number of Fisher Scoring iterations: 5
data01=subset(data1,data1$Covid=="Reference state of alarm 2" | data1$Covid=="state of alarm 2")
boxplot(data01$euros~data01$Covid, col = rgb(0, 0.5, 1, alpha = 0.5),xlab="Covid Category",ylab="Price")

boxplot(data01$kgs~data01$Covid, col = rgb(0, 0.5, 1, alpha = 0.5),xlab="Covid Category",ylab="Abundance")

x=as.factor(data01$Covid)
table(x)
## x
## Reference state of alarm 2           state of alarm 2 
##                         76                        112
y=as.numeric(data01$euros)
x=relevel(x, ref = "Reference state of alarm 2")
z=as.numeric(data01$kgs)


Model01<-glm((y)~x,family=Gamma(link=log))
cooksd <- cooks.distance(Model01)
influential <- cooksd[(cooksd > (3 * mean(cooksd, na.rm = TRUE)))]
influential=as.numeric(names(influential))
y=y[-influential]
xaux=x[-influential]
Model01<-glm((y)~xaux,family=Gamma(link=log))
summary(Model01)
## 
## Call:
## glm(formula = (y) ~ xaux, family = Gamma(link = log))
## 
## Deviance Residuals: 
##      Min        1Q    Median        3Q       Max  
## -0.60295  -0.16905  -0.05135   0.13961   0.53584  
## 
## Coefficients:
##                      Estimate Std. Error t value Pr(>|t|)    
## (Intercept)           3.69406    0.02714 136.130   <2e-16 ***
## xauxstate of alarm 2  0.03487    0.03537   0.986    0.326    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for Gamma family taken to be 0.05301924)
## 
##     Null deviance: 9.1333  on 174  degrees of freedom
## Residual deviance: 9.0818  on 173  degrees of freedom
## AIC: 1277.4
## 
## Number of Fisher Scoring iterations: 4
Model01<-glm((z)~x,family=Gamma(link=log))
cooksd <- cooks.distance(Model01)
influential <- cooksd[(cooksd > (3 * mean(cooksd, na.rm = TRUE)))]
influential=as.numeric(names(influential))
z=z[-influential]
xaux=x[-influential]

Model01<-glm((z)~xaux,family=Gamma(link=log))
summary(Model01)
## 
## Call:
## glm(formula = (z) ~ xaux, family = Gamma(link = log))
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -3.2551  -0.5234   0.0706   0.3549   1.0003  
## 
## Coefficients:
##                      Estimate Std. Error t value Pr(>|t|)    
## (Intercept)           5.95033    0.06427  92.588  < 2e-16 ***
## xauxstate of alarm 2 -0.30834    0.08359  -3.689 0.000299 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for Gamma family taken to be 0.3056335)
## 
##     Null deviance: 142.53  on 180  degrees of freedom
## Residual deviance: 138.30  on 179  degrees of freedom
## AIC: 2443.3
## 
## Number of Fisher Scoring iterations: 6
data02=subset(data1,data1$Covid=="Reference after state of alarm" | data1$Covid=="after state of alarm")
boxplot(data02$euros~data02$Covid, col = rgb(0, 0.5, 1, alpha = 0.5),xlab="Covid Category",ylab="Price")

boxplot(data02$kgs~data02$Covid, col = rgb(0, 0.5, 1, alpha = 0.5),xlab="Covid Category",ylab="Abundance")

x=as.factor(data02$Covid)
table(x)
## x
##           after state of alarm Reference after state of alarm 
##                             90                             90
y=as.numeric(data02$euros)
x=relevel(x, ref = "Reference after state of alarm")
z=as.numeric(data02$kgs)

Model02<-glm((y)~x,family=Gamma(link=log))
cooksd <- cooks.distance(Model02)
influential <- cooksd[(cooksd > (3 * mean(cooksd, na.rm = TRUE)))]
influential=as.numeric(names(influential))
y=y[-influential]
xaux=x[-influential]
Model02<-glm((y)~xaux,family=Gamma(link=log))
summary(Model02)
## 
## Call:
## glm(formula = (y) ~ xaux, family = Gamma(link = log))
## 
## Deviance Residuals: 
##       Min         1Q     Median         3Q        Max  
## -0.257685  -0.070497   0.005646   0.068994   0.208254  
## 
## Coefficients:
##                          Estimate Std. Error t value Pr(>|t|)    
## (Intercept)               3.54535    0.01206 294.026  < 2e-16 ***
## xauxafter state of alarm  0.10878    0.01666   6.529 7.71e-10 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for Gamma family taken to be 0.01163157)
## 
##     Null deviance: 2.4632  on 167  degrees of freedom
## Residual deviance: 1.9684  on 166  degrees of freedom
## AIC: 944.45
## 
## Number of Fisher Scoring iterations: 4
Model02<-glm((z)~x,family=Gamma(link=log))
cooksd <- cooks.distance(Model02)
influential <- cooksd[(cooksd > (3 * mean(cooksd, na.rm = TRUE)))]
influential=as.numeric(names(influential))
z=z[-influential]
xaux=x[-influential]

Model02<-glm((z)~xaux,family=Gamma(link=log))
summary(Model02)
## 
## Call:
## glm(formula = (z) ~ xaux, family = Gamma(link = log))
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -1.6466  -0.5820  -0.1271   0.3745   1.0320  
## 
## Coefficients:
##                          Estimate Std. Error t value Pr(>|t|)    
## (Intercept)               6.50239    0.06470 100.501   <2e-16 ***
## xauxafter state of alarm  0.05779    0.09150   0.632    0.529    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for Gamma family taken to be 0.3558118)
## 
##     Null deviance: 68.813  on 169  degrees of freedom
## Residual deviance: 68.671  on 168  degrees of freedom
## AIC: 2497.7
## 
## Number of Fisher Scoring iterations: 5

3.15 Sardinella aurita

data1=subset(data_M,data_M$specie=="SAA-ALACHA")

Is gamma a correct distribution for the response variable PRICE?

summary(data1$euros)
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
## 0.05486 0.26206 0.29521 0.42460 0.44265 3.46262
a=fitdist(data1$euros, distr = "gamma",start=list(shape = 1, rate = 2), lower = -1,method = "mme")
plot(a)

ks.test(na.omit(data1$euros), "pgamma", a$estimate[1],a$estimate[2])
## 
##  One-sample Kolmogorov-Smirnov test
## 
## data:  na.omit(data1$euros)
## D = 0.27523, p-value < 2.2e-16
## alternative hypothesis: two-sided
gamma_test(data1$euros)
## 
##  Test of fit for the Gamma distribution
## 
## data:  data1$euros
## V = 25.915, p-value < 2.2e-16

Is gamma a correct distribution for the response variable ABUNDANCE?

summary(data1$kgs)
##      Min.   1st Qu.    Median      Mean   3rd Qu.      Max. 
##      3.95   1648.86   5150.10  10582.78  11770.38 190873.60
a=fitdist(data1$kgs, distr = "gamma",start=list(shape = 1, rate = 2), lower = -1,method = "mme")
plot(a)

ks.test(na.omit(data1$kgs), "pgamma", a$estimate[1],a$estimate[2])
## 
##  One-sample Kolmogorov-Smirnov test
## 
## data:  na.omit(data1$kgs)
## D = 0.16814, p-value = 5.986e-13
## alternative hypothesis: two-sided
gamma_test(data1$kgs)
## 
##  Test of fit for the Gamma distribution
## 
## data:  data1$kgs
## V = 9.4068, p-value = 2.899e-11

Regression models

x=as.factor(data1$Covid)
levels(x)
## [1] "after state of alarm"           "Reference after state of alarm"
## [3] "Reference No"                   "Reference state of alarm 1"    
## [5] "Reference state of alarm 2"     "state of alarm 1"              
## [7] "state of alarm 2"
data0=subset(data1,data1$Covid=="Reference state of alarm 1" | data1$Covid=="state of alarm 1")
boxplot(data0$euros~data0$Covid, col = rgb(0, 0.5, 1, alpha = 0.5),xlab="Covid Category",ylab="Price")

boxplot(data0$kgs~data0$Covid, col = rgb(0, 0.5, 1, alpha = 0.5),xlab="Covid Category",ylab="Abundance")

x=as.factor(data0$Covid)
table(x)
## x
## Reference state of alarm 1           state of alarm 1 
##                         59                         43
y=as.numeric(data0$euros)
x=relevel(x, ref = "Reference state of alarm 1")
z=as.numeric(data0$kgs)

Model0<-glm((y)~x,family=Gamma(link=log))
cooksd <- cooks.distance(Model0)
influential <- cooksd[(cooksd > (3 * mean(cooksd, na.rm = TRUE)))]
influential=as.numeric(names(influential))
y=y[-influential]
xaux=x[-influential]
Model0<-glm((y)~xaux,family=Gamma(link=log))
summary(Model0)
## 
## Call:
## glm(formula = (y) ~ xaux, family = Gamma(link = log))
## 
## Deviance Residuals: 
##      Min        1Q    Median        3Q       Max  
## -0.87722  -0.24628  -0.09057   0.10428   0.74847  
## 
## Coefficients:
##                      Estimate Std. Error t value Pr(>|t|)    
## (Intercept)          -1.12773    0.04304 -26.204   <2e-16 ***
## xauxstate of alarm 1  0.04894    0.06717   0.729    0.468    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for Gamma family taken to be 0.1037207)
## 
##     Null deviance: 9.0614  on 94  degrees of freedom
## Residual deviance: 9.0062  on 93  degrees of freedom
## AIC: -166.18
## 
## Number of Fisher Scoring iterations: 4
Model0<-glm((z)~x,family=Gamma(link=log))
cooksd <- cooks.distance(Model0)
influential <- cooksd[(cooksd > (3 * mean(cooksd, na.rm = TRUE)))]
influential=as.numeric(names(influential))
z=z[-influential]
xaux=x[-influential]

Model0<-glm((z)~xaux,family=Gamma(link=log))
summary(Model0)
## 
## Call:
## glm(formula = (z) ~ xaux, family = Gamma(link = log))
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -3.3444  -1.1730  -0.3655   0.6154   1.5323  
## 
## Coefficients:
##                      Estimate Std. Error t value Pr(>|t|)    
## (Intercept)            8.4552     0.1257  67.265   <2e-16 ***
## xauxstate of alarm 1  -0.1682     0.1923  -0.874    0.384    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for Gamma family taken to be 0.8690168)
## 
##     Null deviance: 153.42  on 95  degrees of freedom
## Residual deviance: 152.76  on 94  degrees of freedom
## AIC: 1803.9
## 
## Number of Fisher Scoring iterations: 7
data01=subset(data1,data1$Covid=="Reference state of alarm 2" | data1$Covid=="state of alarm 2")
boxplot(data01$euros~data01$Covid, col = rgb(0, 0.5, 1, alpha = 0.5),xlab="Covid Category",ylab="Price")

boxplot(data01$kgs~data01$Covid, col = rgb(0, 0.5, 1, alpha = 0.5),xlab="Covid Category",ylab="Abundance")

x=as.factor(data01$Covid)
table(x)
## x
## Reference state of alarm 2           state of alarm 2 
##                         84                        110
y=as.numeric(data01$euros)
x=relevel(x, ref = "Reference state of alarm 2")
z=as.numeric(data01$kgs)


Model01<-glm((y)~x,family=Gamma(link=log))
cooksd <- cooks.distance(Model01)
influential <- cooksd[(cooksd > (3 * mean(cooksd, na.rm = TRUE)))]
influential=as.numeric(names(influential))
y=y[-influential]
xaux=x[-influential]
Model01<-glm((y)~xaux,family=Gamma(link=log))
summary(Model01)
## 
## Call:
## glm(formula = (y) ~ xaux, family = Gamma(link = log))
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -1.0750  -0.3630  -0.2039   0.2065   1.0258  
## 
## Coefficients:
##                      Estimate Std. Error t value Pr(>|t|)    
## (Intercept)          -0.74375    0.05340 -13.928  < 2e-16 ***
## xauxstate of alarm 2 -0.25157    0.07064  -3.561 0.000472 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for Gamma family taken to be 0.2224307)
## 
##     Null deviance: 37.915  on 181  degrees of freedom
## Residual deviance: 35.067  on 180  degrees of freedom
## AIC: -129.5
## 
## Number of Fisher Scoring iterations: 4
Model01<-glm((z)~x,family=Gamma(link=log))
cooksd <- cooks.distance(Model01)
influential <- cooksd[(cooksd > (3 * mean(cooksd, na.rm = TRUE)))]
influential=as.numeric(names(influential))
z=z[-influential]
xaux=x[-influential]

Model01<-glm((z)~xaux,family=Gamma(link=log))
summary(Model01)
## 
## Call:
## glm(formula = (z) ~ xaux, family = Gamma(link = log))
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -3.4713  -1.1401  -0.3101   0.5271   1.6272  
## 
## Coefficients:
##                      Estimate Std. Error t value Pr(>|t|)    
## (Intercept)            8.2202     0.1072  76.688  < 2e-16 ***
## xauxstate of alarm 2   0.6786     0.1405   4.831 2.89e-06 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for Gamma family taken to be 0.8732366)
## 
##     Null deviance: 315.23  on 181  degrees of freedom
## Residual deviance: 295.93  on 180  degrees of freedom
## AIC: 3497.5
## 
## Number of Fisher Scoring iterations: 6
data02=subset(data1,data1$Covid=="Reference after state of alarm" | data1$Covid=="after state of alarm")
boxplot(data02$euros~data02$Covid, col = rgb(0, 0.5, 1, alpha = 0.5),xlab="Covid Category",ylab="Price")

boxplot(data02$kgs~data02$Covid, col = rgb(0, 0.5, 1, alpha = 0.5),xlab="Covid Category",ylab="Abundance")

x=as.factor(data02$Covid)
table(x)
## x
##           after state of alarm Reference after state of alarm 
##                             86                             83
y=as.numeric(data02$euros)
x=relevel(x, ref = "Reference after state of alarm")
z=as.numeric(data02$kgs)

Model02<-glm((y)~x,family=Gamma(link=log))
cooksd <- cooks.distance(Model02)
influential <- cooksd[(cooksd > (3 * mean(cooksd, na.rm = TRUE)))]
influential=as.numeric(names(influential))
y=y[-influential]
xaux=x[-influential]
Model02<-glm((y)~xaux,family=Gamma(link=log))
summary(Model02)
## 
## Call:
## glm(formula = (y) ~ xaux, family = Gamma(link = log))
## 
## Deviance Residuals: 
##      Min        1Q    Median        3Q       Max  
## -0.55305  -0.08895  -0.05182   0.01503   0.92249  
## 
## Coefficients:
##                          Estimate Std. Error t value Pr(>|t|)    
## (Intercept)              -1.21982    0.02591 -47.072   <2e-16 ***
## xauxafter state of alarm -0.06346    0.03621  -1.753   0.0816 .  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for Gamma family taken to be 0.05372383)
## 
##     Null deviance: 7.0404  on 163  degrees of freedom
## Residual deviance: 6.8753  on 162  degrees of freedom
## AIC: -465.28
## 
## Number of Fisher Scoring iterations: 4
Model02<-glm((z)~x,family=Gamma(link=log))
cooksd <- cooks.distance(Model02)
influential <- cooksd[(cooksd > (3 * mean(cooksd, na.rm = TRUE)))]
influential=as.numeric(names(influential))
z=z[-influential]
xaux=x[-influential]

Model02<-glm((z)~xaux,family=Gamma(link=log))
summary(Model02)
## 
## Call:
## glm(formula = (z) ~ xaux, family = Gamma(link = log))
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -3.4746  -1.0080  -0.3529   0.3430   1.8570  
## 
## Coefficients:
##                          Estimate Std. Error t value Pr(>|t|)    
## (Intercept)                9.0129     0.1122  80.338  < 2e-16 ***
## xauxafter state of alarm   0.8559     0.1576   5.429 2.16e-07 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for Gamma family taken to be 0.9691201)
## 
##     Null deviance: 232.39  on 155  degrees of freedom
## Residual deviance: 204.76  on 154  degrees of freedom
## AIC: 3266.3
## 
## Number of Fisher Scoring iterations: 6

3.16 Thunnus thynnus

data1=subset(data_M,data_M$specie=="BFT-ATUN ROJO" )

Is gamma a correct distribution for the response variable PRICE?

summary(data1$euros)
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##   6.123   8.526   9.006   9.416  10.106  15.373
a=fitdist(data1$euros, distr = "gamma",start=list(shape = 1, rate = 2), lower = -1,method = "mme")
plot(a)

ks.test(na.omit(data1$euros), "pgamma", a$estimate[1],a$estimate[2])
## 
##  One-sample Kolmogorov-Smirnov test
## 
## data:  na.omit(data1$euros)
## D = 0.12966, p-value = 6.147e-07
## alternative hypothesis: two-sided
gamma_test(data1$euros)
## 
##  Test of fit for the Gamma distribution
## 
## data:  data1$euros
## V = 9.9871, p-value = 1.642e-12

Is gamma a correct distribution for the response variable ABUNDANCE?

summary(data1$kgs)
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##    57.0   663.1  1499.0  1869.1  2670.2  8310.6
a=fitdist(data1$kgs, distr = "gamma",start=list(shape = 1, rate = 2), lower = -1,method = "mme")
plot(a)

ks.test(na.omit(data1$kgs), "pgamma", a$estimate[1],a$estimate[2])
## 
##  One-sample Kolmogorov-Smirnov test
## 
## data:  na.omit(data1$kgs)
## D = 0.061161, p-value = 0.07111
## alternative hypothesis: two-sided
gamma_test(data1$kgs)
## 
##  Test of fit for the Gamma distribution
## 
## data:  data1$kgs
## V = -3.158, p-value = 0.02555

Regression models

x=as.factor(data1$Covid)
levels(x)
## [1] "after state of alarm"           "Reference after state of alarm"
## [3] "Reference No"                   "Reference state of alarm 1"    
## [5] "Reference state of alarm 2"     "state of alarm 1"              
## [7] "state of alarm 2"
data0=subset(data1,data1$Covid=="Reference state of alarm 1" | data1$Covid=="state of alarm 1")
boxplot(data0$euros~data0$Covid, col = rgb(0, 0.5, 1, alpha = 0.5),xlab="Covid Category",ylab="Price")

boxplot(data0$kgs~data0$Covid, col = rgb(0, 0.5, 1, alpha = 0.5),xlab="Covid Category",ylab="Abundance")

x=as.factor(data0$Covid)
table(x)
## x
## Reference state of alarm 1           state of alarm 1 
##                         42                         30
y=as.numeric(data0$euros)
x=relevel(x, ref = "Reference state of alarm 1")
z=as.numeric(data0$kgs)

Model0<-glm((y)~x,family=Gamma(link=log))
cooksd <- cooks.distance(Model0)
influential <- cooksd[(cooksd > (3 * mean(cooksd, na.rm = TRUE)))]
influential=as.numeric(names(influential))
y=y[-influential]
xaux=x[-influential]
Model0<-glm((y)~xaux,family=Gamma(link=log))
summary(Model0)
## 
## Call:
## glm(formula = (y) ~ xaux, family = Gamma(link = log))
## 
## Deviance Residuals: 
##      Min        1Q    Median        3Q       Max  
## -0.18283  -0.08413  -0.01410   0.07488   0.25586  
## 
## Coefficients:
##                      Estimate Std. Error t value Pr(>|t|)    
## (Intercept)           2.26801    0.01735 130.726   <2e-16 ***
## xauxstate of alarm 1 -0.07106    0.02785  -2.552   0.0131 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for Gamma family taken to be 0.01234095)
## 
##     Null deviance: 0.86005  on 66  degrees of freedom
## Residual deviance: 0.78014  on 65  degrees of freedom
## AIC: 197.36
## 
## Number of Fisher Scoring iterations: 4
Model0<-glm((z)~x,family=Gamma(link=log))
cooksd <- cooks.distance(Model0)
influential <- cooksd[(cooksd > (3 * mean(cooksd, na.rm = TRUE)))]
influential=as.numeric(names(influential))
z=z[-influential]
xaux=x[-influential]

Model0<-glm((z)~xaux,family=Gamma(link=log))
summary(Model0)
## 
## Call:
## glm(formula = (z) ~ xaux, family = Gamma(link = log))
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -1.5157  -1.0607  -0.2301   0.5340   1.4946  
## 
## Coefficients:
##                      Estimate Std. Error t value Pr(>|t|)    
## (Intercept)            6.9474     0.1246  55.751  < 2e-16 ***
## xauxstate of alarm 1  -0.8890     0.1956  -4.544 2.36e-05 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for Gamma family taken to be 0.6366878)
## 
##     Null deviance: 63.125  on 68  degrees of freedom
## Residual deviance: 51.030  on 67  degrees of freedom
## AIC: 1047.3
## 
## Number of Fisher Scoring iterations: 5
data01=subset(data1,data1$Covid=="Reference state of alarm 2" | data1$Covid=="state of alarm 2")
boxplot(data01$euros~data01$Covid, col = rgb(0, 0.5, 1, alpha = 0.5),xlab="Covid Category",ylab="Price")

boxplot(data01$kgs~data01$Covid, col = rgb(0, 0.5, 1, alpha = 0.5),xlab="Covid Category",ylab="Abundance")

x=as.factor(data01$Covid)
table(x)
## x
## Reference state of alarm 2           state of alarm 2 
##                         72                        102
y=as.numeric(data01$euros)
x=relevel(x, ref = "Reference state of alarm 2")
z=as.numeric(data01$kgs)


Model01<-glm((y)~x,family=Gamma(link=log))
cooksd <- cooks.distance(Model01)
influential <- cooksd[(cooksd > (3 * mean(cooksd, na.rm = TRUE)))]
influential=as.numeric(names(influential))
y=y[-influential]
xaux=x[-influential]
Model01<-glm((y)~xaux,family=Gamma(link=log))
summary(Model01)
## 
## Call:
## glm(formula = (y) ~ xaux, family = Gamma(link = log))
## 
## Deviance Residuals: 
##      Min        1Q    Median        3Q       Max  
## -0.17930  -0.04547  -0.01187   0.04427   0.19930  
## 
## Coefficients:
##                       Estimate Std. Error t value Pr(>|t|)    
## (Intercept)           2.318479   0.009381  247.15   <2e-16 ***
## xauxstate of alarm 2 -0.162466   0.011903  -13.65   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for Gamma family taken to be 0.005368212)
## 
##     Null deviance: 1.86091  on 160  degrees of freedom
## Residual deviance: 0.84865  on 159  degrees of freedom
## AIC: 331.72
## 
## Number of Fisher Scoring iterations: 3
Model01<-glm((z)~x,family=Gamma(link=log))
cooksd <- cooks.distance(Model01)
influential <- cooksd[(cooksd > (3 * mean(cooksd, na.rm = TRUE)))]
influential=as.numeric(names(influential))
z=z[-influential]
xaux=x[-influential]

Model01<-glm((z)~xaux,family=Gamma(link=log))
summary(Model01)
## 
## Call:
## glm(formula = (z) ~ xaux, family = Gamma(link = log))
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -2.1561  -0.7852  -0.1035   0.4970   1.1759  
## 
## Coefficients:
##                      Estimate Std. Error t value Pr(>|t|)    
## (Intercept)           7.43229    0.08323  89.303   <2e-16 ***
## xauxstate of alarm 2 -0.04267    0.10887  -0.392    0.696    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for Gamma family taken to be 0.4779315)
## 
##     Null deviance: 126.76  on 165  degrees of freedom
## Residual deviance: 126.68  on 164  degrees of freedom
## AIC: 2785.5
## 
## Number of Fisher Scoring iterations: 5
data02=subset(data1,data1$Covid=="Reference after state of alarm" | data1$Covid=="after state of alarm")
boxplot(data02$euros~data02$Covid, col = rgb(0, 0.5, 1, alpha = 0.5),xlab="Covid Category",ylab="Price")

boxplot(data02$kgs~data02$Covid, col = rgb(0, 0.5, 1, alpha = 0.5),xlab="Covid Category",ylab="Abundance")

x=as.factor(data02$Covid)
table(x)
## x
##           after state of alarm Reference after state of alarm 
##                             82                             83
y=as.numeric(data02$euros)
x=relevel(x, ref = "Reference after state of alarm")
z=as.numeric(data02$kgs)

Model02<-glm((y)~x,family=Gamma(link=log))
cooksd <- cooks.distance(Model02)
influential <- cooksd[(cooksd > (3 * mean(cooksd, na.rm = TRUE)))]
influential=as.numeric(names(influential))
y=y[-influential]
xaux=x[-influential]
Model02<-glm((y)~xaux,family=Gamma(link=log))
summary(Model02)
## 
## Call:
## glm(formula = (y) ~ xaux, family = Gamma(link = log))
## 
## Deviance Residuals: 
##       Min         1Q     Median         3Q        Max  
## -0.170133  -0.061647  -0.001852   0.041470   0.172476  
## 
## Coefficients:
##                          Estimate Std. Error t value Pr(>|t|)    
## (Intercept)               2.27469    0.00873 260.555  < 2e-16 ***
## xauxafter state of alarm -0.09131    0.01211  -7.543  4.3e-12 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for Gamma family taken to be 0.005487536)
## 
##     Null deviance: 1.11685  on 149  degrees of freedom
## Residual deviance: 0.80439  on 148  degrees of freedom
## AIC: 314.93
## 
## Number of Fisher Scoring iterations: 3
Model02<-glm((z)~x,family=Gamma(link=log))
cooksd <- cooks.distance(Model02)
influential <- cooksd[(cooksd > (3 * mean(cooksd, na.rm = TRUE)))]
influential=as.numeric(names(influential))
z=z[-influential]
xaux=x[-influential]

Model02<-glm((z)~xaux,family=Gamma(link=log))
summary(Model02)
## 
## Call:
## glm(formula = (z) ~ xaux, family = Gamma(link = log))
## 
## Deviance Residuals: 
##      Min        1Q    Median        3Q       Max  
## -2.19075  -0.72654  -0.06427   0.37713   1.19940  
## 
## Coefficients:
##                          Estimate Std. Error t value Pr(>|t|)    
## (Intercept)               7.46004    0.07618  97.920   <2e-16 ***
## xauxafter state of alarm  0.16778    0.10774   1.557    0.122    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for Gamma family taken to be 0.4411121)
## 
##     Null deviance: 100.675  on 151  degrees of freedom
## Residual deviance:  99.607  on 150  degrees of freedom
## AIC: 2583.2
## 
## Number of Fisher Scoring iterations: 5

3.17 Auxis rochei

data1=subset(data_M,data_M$specie=="BLT-MELVA")

Is gamma a correct distribution for the response variable PRICE?

summary(data1$euros)
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##  0.8725  1.1759  1.5087  1.9980  2.6806  5.2000
a=fitdist(data1$euros, distr = "gamma",start=list(shape = 1, rate = 2), lower = -1,method = "mme")
plot(a)

ks.test(na.omit(data1$euros), "pgamma", a$estimate[1],a$estimate[2])
## 
##  One-sample Kolmogorov-Smirnov test
## 
## data:  na.omit(data1$euros)
## D = 0.17954, p-value = 1.711e-05
## alternative hypothesis: two-sided
gamma_test(data1$euros)
## 
##  Test of fit for the Gamma distribution
## 
## data:  data1$euros
## V = 4.0454, p-value = 0.00423

Is gamma a correct distribution for the response variable ABUNDANCE?

summary(data1$kgs)
##     Min.  1st Qu.   Median     Mean  3rd Qu.     Max. 
##     0.90     9.18   113.10  2896.89  2790.00 54174.79
a=fitdist(data1$kgs, distr = "gamma",start=list(shape = 1, rate = 2), lower = -1,method = "mme")
plot(a)

ks.test(na.omit(data1$kgs), "pgamma", a$estimate[1],a$estimate[2])
## 
##  One-sample Kolmogorov-Smirnov test
## 
## data:  na.omit(data1$kgs)
## D = 0.17142, p-value = 4.803e-05
## alternative hypothesis: two-sided
gamma_test(data1$kgs)
## 
##  Test of fit for the Gamma distribution
## 
## data:  data1$kgs
## V = 1.2951, p-value = 0.3598

Regression models

x=as.factor(data1$Covid)
levels(x)
## [1] "after state of alarm"           "Reference after state of alarm"
## [3] "Reference No"                   "Reference state of alarm 1"    
## [5] "Reference state of alarm 2"     "state of alarm 1"              
## [7] "state of alarm 2"
data0=subset(data1,data1$Covid=="Reference state of alarm 1" | data1$Covid=="state of alarm 1")
boxplot(data0$euros~data0$Covid, col = rgb(0, 0.5, 1, alpha = 0.5),xlab="Covid Category",ylab="Price")

boxplot(data0$kgs~data0$Covid, col = rgb(0, 0.5, 1, alpha = 0.5),xlab="Covid Category",ylab="Abundance")

x=as.factor(data0$Covid)
table(x)
## x
## Reference state of alarm 1           state of alarm 1 
##                         10                          1
y=as.numeric(data0$euros)
x=relevel(x, ref = "Reference state of alarm 1")
z=as.numeric(data0$kgs)

Model0<-glm((y)~x,family=Gamma(link=log))
cooksd <- cooks.distance(Model0)
influential <- cooksd[(cooksd > (3 * mean(cooksd, na.rm = TRUE)))]
influential=as.numeric(names(influential))[!is.na(as.numeric(names(influential)))]
y=y[-influential]
xaux=x[-influential]
Model0<-glm((y)~xaux,family=Gamma(link=log))
summary(Model0)
## 
## Call:
## glm(formula = (y) ~ xaux, family = Gamma(link = log))
## 
## Deviance Residuals: 
##       Min         1Q     Median         3Q        Max  
## -0.142814  -0.039267   0.009279   0.068721   0.089071  
## 
## Coefficients:
##                      Estimate Std. Error t value Pr(>|t|)    
## (Intercept)           1.42582    0.02993  47.640  4.7e-10 ***
## xauxstate of alarm 1 -0.84465    0.08979  -9.407  3.2e-05 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for Gamma family taken to be 0.007166109)
## 
##     Null deviance: 0.562693  on 8  degrees of freedom
## Residual deviance: 0.051721  on 7  degrees of freedom
## AIC: 9.0413
## 
## Number of Fisher Scoring iterations: 3
Model0<-glm((z)~x,family=Gamma(link=log))
cooksd <- cooks.distance(Model0)
influential <- cooksd[(cooksd > (3 * mean(cooksd, na.rm = TRUE)))]
influential=as.numeric(names(influential))[!is.na(as.numeric(names(influential)))]
z=z[-influential]
xaux=x[-influential]

if(length(z)>0){
Model0<-glm((z)~xaux,family=Gamma(link=log))
summary(Model0)}

data01=subset(data1,data1$Covid=="Reference state of alarm 2" | data1$Covid=="state of alarm 2")
boxplot(data01$euros~data01$Covid, col = rgb(0, 0.5, 1, alpha = 0.5),xlab="Covid Category",ylab="Price")

boxplot(data01$kgs~data01$Covid, col = rgb(0, 0.5, 1, alpha = 0.5),xlab="Covid Category",ylab="Abundance")

x=as.factor(data01$Covid)
table(x)
## x
## Reference state of alarm 2           state of alarm 2 
##                         22                         40
y=as.numeric(data01$euros)
x=relevel(x, ref = "Reference state of alarm 2")
z=as.numeric(data01$kgs)


Model01<-glm((y)~x,family=Gamma(link=log))
cooksd <- cooks.distance(Model01)
influential <- cooksd[(cooksd > (3 * mean(cooksd, na.rm = TRUE)))]
influential=as.numeric(names(influential))
y=y[-influential]
xaux=x[-influential]
Model01<-glm((y)~xaux,family=Gamma(link=log))
summary(Model01)
## 
## Call:
## glm(formula = (y) ~ xaux, family = Gamma(link = log))
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -0.7686  -0.3941  -0.1351   0.3150   0.6548  
## 
## Coefficients:
##                      Estimate Std. Error t value Pr(>|t|)    
## (Intercept)           0.29684    0.08705   3.410 0.001200 ** 
## xauxstate of alarm 2  0.44769    0.10707   4.181 0.000101 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for Gamma family taken to be 0.1515671)
## 
##     Null deviance: 11.1388  on 58  degrees of freedom
## Residual deviance:  8.6295  on 57  degrees of freedom
## AIC: 122.77
## 
## Number of Fisher Scoring iterations: 4
Model01<-glm((z)~x,family=Gamma(link=log))
cooksd <- cooks.distance(Model01)
influential <- cooksd[(cooksd > (3 * mean(cooksd, na.rm = TRUE)))]
influential=as.numeric(names(influential))
z=z[-influential]
xaux=x[-influential]

Model01<-glm((z)~xaux,family=Gamma(link=log))
summary(Model01)
## 
## Call:
## glm(formula = (z) ~ xaux, family = Gamma(link = log))
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -3.5698  -2.1820  -1.1725   0.0361   3.6434  
## 
## Coefficients:
##                      Estimate Std. Error t value Pr(>|t|)    
## (Intercept)            7.3712     0.3808  19.357  < 2e-16 ***
## xauxstate of alarm 2  -2.8032     0.4785  -5.858 2.33e-07 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for Gamma family taken to be 3.190303)
## 
##     Null deviance: 315.73  on 59  degrees of freedom
## Residual deviance: 211.14  on 58  degrees of freedom
## AIC: 741.71
## 
## Number of Fisher Scoring iterations: 10
data02=subset(data1,data1$Covid=="Reference after state of alarm" | data1$Covid=="after state of alarm")
boxplot(data02$euros~data02$Covid, col = rgb(0, 0.5, 1, alpha = 0.5),xlab="Covid Category",ylab="Price")

boxplot(data02$kgs~data02$Covid, col = rgb(0, 0.5, 1, alpha = 0.5),xlab="Covid Category",ylab="Abundance")

x=as.factor(data02$Covid)
table(x)
## x
##           after state of alarm Reference after state of alarm 
##                             45                             58
y=as.numeric(data02$euros)
x=relevel(x, ref = "Reference after state of alarm")
z=as.numeric(data02$kgs)

Model02<-glm((y)~x,family=Gamma(link=log))
cooksd <- cooks.distance(Model02)
influential <- cooksd[(cooksd > (3 * mean(cooksd, na.rm = TRUE)))]
influential=as.numeric(names(influential))
y=y[-influential]
xaux=x[-influential]
Model02<-glm((y)~xaux,family=Gamma(link=log))
summary(Model02)
## 
## Call:
## glm(formula = (y) ~ xaux, family = Gamma(link = log))
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -0.4536  -0.2900  -0.1123   0.1371   0.7177  
## 
## Coefficients:
##                          Estimate Std. Error t value Pr(>|t|)    
## (Intercept)               0.49069    0.04579  10.717   <2e-16 ***
## xauxafter state of alarm -0.10285    0.06734  -1.527     0.13    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for Gamma family taken to be 0.1048202)
## 
##     Null deviance: 8.5226  on 92  degrees of freedom
## Residual deviance: 8.2788  on 91  degrees of freedom
## AIC: 120.49
## 
## Number of Fisher Scoring iterations: 4
Model02<-glm((z)~x,family=Gamma(link=log))
cooksd <- cooks.distance(Model02)
influential <- cooksd[(cooksd > (3 * mean(cooksd, na.rm = TRUE)))]
influential=as.numeric(names(influential))
z=z[-influential]
xaux=x[-influential]

Model02<-glm((z)~xaux,family=Gamma(link=log))
summary(Model02)
## 
## Call:
## glm(formula = (z) ~ xaux, family = Gamma(link = log))
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -3.5612  -2.7284  -1.6420   0.4866   2.3205  
## 
## Coefficients:
##                          Estimate Std. Error t value Pr(>|t|)    
## (Intercept)                7.3405     0.1985  36.976  < 2e-16 ***
## xauxafter state of alarm   1.1455     0.2986   3.836 0.000227 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for Gamma family taken to be 2.088828)
## 
##     Null deviance: 434.98  on 94  degrees of freedom
## Residual deviance: 404.52  on 93  degrees of freedom
## AIC: 1554
## 
## Number of Fisher Scoring iterations: 14